"#include "pch.h" #define XOR_RAND(state, result_...

创建于:2026年2月3日

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"#include "pch.h"

#define XOR_RAND(state, result_var)
do {
unsigned s = state;
s ^= s << 13;
s ^= s >> 17;
s ^= s << 5;
state = s;
float tmp = static_cast<float>(static_cast<double>(s) *
(1.0 / 4294967296.0));
result_var = tmp;
} while (0)

#define XOR_RAND_GRSH(state, result_var)
do {
unsigned s = state;
s ^= s << 13;
s ^= s >> 17;
s ^= s << 5;
state = s;
result_var =
fmaf(static_cast<float>(static_cast<int>(s)), 0x1.0p-31f, -1.0f);
} while (0)

#define FABE13_COS(x, result_var)
do {
const float ax = fabsf(x);
float r = fmodf(ax, 6.28318530717958647692f);
if (r > 3.14159265359f)
r = 6.28318530717958647692f - r;
if (r < 1.57079632679f) {
const float t2 = r * r;
const float t4 = t2 * t2;
result_var =
fmaf(t4,
fmaf(t2, -0.0013888889f, 0.0416666667f),
fmaf(t2, -0.5f, 1.0f));
} else {
r = 3.14159265359f - r;
const float t2 = r * r;
const float t4 = t2 * t2;
result_var =
-fmaf(t4,
fmaf(t2, -0.0013888889f, 0.041666667f),
fmaf(t2, -0.5f, 1.0f));
}
} while (0)

#define FABE13_SIN(x, result_var)
do {
const float x = x;
const float ax = fabsf(x);
float r = fmodf(ax, 6.28318530717958647692f);
bool sfl = r > 3.14159265359f;
if (sfl)
r = 6.28318530717958647692f - r;
bool cfl = r > 1.57079632679f;
if (cfl)
r = 3.14159265359f - r;
const float t2 = r * r;
float _s = fmaf(t2,
fmaf(t2,
fmaf(t2, -0.0001984127f, 0.0083333333f),
-0.16666666f),
1.0f) *
r;
result_var = ((x < 0.0f) ^ sfl) ? -_s : _s;
} while (0)

#define FABE13_SINCOS(in, sin_out, cos_out, n)
do {
int i = 0;
const int limit = n & ~7;
if (n >= 8) {
static __declspec(align(16)) const __m256 VEC_TWOPI =
_mm256_set1_ps(6.28318530717958647692f);
static __declspec(align(16)) const __m256 VEC_PI =
_mm256_set1_ps(3.14159265359f);
static __declspec(align(16)) const __m256 VEC_PI_2 =
_mm256_set1_ps(1.57079632679f);
static __declspec(align(16)) const __m256 INV_TWOPI =
_mm256_set1_ps(0.15915494309189535f);
static __declspec(align(16)) const __m256 BIAS =
_mm256_set1_ps(12582912.0f);
static __declspec(align(16)) const __m256 VEC_COS_P5 =
_mm256_set1_ps(-0.0013888889f);
static __declspec(align(16)) const __m256 VEC_COS_P3 =
_mm256_set1_ps(0.0416666667f);
static __declspec(align(16)) const __m256 VEC_COS_P1 =
_mm256_set1_ps(-0.5f);
static __declspec(align(16)) const __m256 VEC_COS_P0 =
_mm256_set1_ps(1.0f);
static __declspec(align(16)) const __m256 VEC_SIN_P5 =
_mm256_set1_ps(-0.0001984127f);
static __declspec(align(16)) const __m256 VEC_SIN_P3 =
_mm256_set1_ps(0.0083333333f);
static __declspec(align(16)) const __m256 VEC_SIN_P1 =
_mm256_set1_ps(-0.16666666f);
static __declspec(align(16)) const __m256 VEC_SIN_P0 =
_mm256_set1_ps(1.0f);
static __declspec(align(16)) const __m256 VEC_ZERO =
_mm256_setzero_ps();
while (i < limit) {
const __m256 vx = _mm256_load_ps(&(in)[i]);
const __m256 vax = _mm256_andnot_ps(_mm256_set1_ps(-0.0f),
vx);
__m256 q = _mm256_fmadd_ps(vax, INV_TWOPI, BIAS);
q = _mm256_sub_ps(q, BIAS);
const __m256 r = _mm256_fnmadd_ps(VEC_TWOPI, q, vax);
const __m256 r1 =
_mm256_min_ps(r, _mm256_sub_ps(VEC_TWOPI, r));
const __m256 r2 =
_mm256_min_ps(r1, _mm256_sub_ps(VEC_PI, r1));
const __m256 t2 = _mm256_mul_ps(r2, r2);
const __m256 cosv =
_mm256_fmadd_ps(t2,
_mm256_fmadd_ps(t2,
_mm256_fmadd_ps(t2,
VEC_COS_P5,
VEC_COS_P3),
VEC_COS_P1),
VEC_COS_P0);
const __m256 sinv =
_mm256_mul_ps(_mm256_fmadd_ps(t2,
_mm256_fmadd_ps(t2,
_mm256_fmadd_ps(t2,
VEC_SIN_P5,
VEC_SIN_P3),
VEC_SIN_P1),
VEC_SIN_P0),
r2);
const __m256 cflip =
_mm256_cmp_ps(r1, VEC_PI_2, _CMP_GT_OQ);
const __m256 sflip =
_mm256_xor_ps(_mm256_cmp_ps(vx, VEC_ZERO, _CMP_LT_OQ),
_mm256_cmp_ps(r, VEC_PI, _CMP_GT_OQ));
_mm256_store_ps(&(cos_out)[i],
_mm256_blendv_ps(cosv,
_mm256_sub_ps(VEC_ZERO,
cosv),
cflip));
_mm256_store_ps(&(sin_out)[i],
_mm256_blendv_ps(sinv,
_mm256_sub_ps(VEC_ZERO,
sinv),
sflip));
i += 8;
}
}
while (i < n) {
const float x = (in)[i];
const float ax = fabsf(x);
float q = fmaf(ax, 0.15915494309189535f, 12582912.0f);
q -= 12582912.0f;
float r = fmaf(-6.28318530718f, q, ax);
const bool sflip = r > 3.14159265359f;
if (sflip)
r = 6.28318530718f - r;
const bool cflip = r > 1.57079632679f;
if (cflip)
r = 3.14159265359f - r;
const float t2 = r * r;
const float c = fmaf(t2,
fmaf(t2,
fmaf(t2, -0.0013888889f, 0.0416666667f),
-0.5f),
1.0f);
const float s = fmaf(t2,
fmaf(t2,
fmaf(t2, -0.0001984127f, 0.0083333333f),
-0.16666666f),
1.0f) *
r;
(cos_out)[i] = cflip ? -c : c;
(sin_out)[i] = ((x < 0.0f) ^ sflip) ? -s : s;
++i;
}
} while (0)

enum List : unsigned {
Top = 0b00u,
Down = 0b01u,
Left = 0b10u,
Right = 0b11u
};

__declspec(align(16)) struct Step final {
const unsigned next;
const unsigned dx;
const unsigned dy;

text
__forceinline Step(unsigned n, unsigned x, unsigned y) noexcept : next(n), dx(x), dy(y) { }

};

__declspec(align(16)) struct InvStep final {
const unsigned q;
const unsigned next;

text
__forceinline InvStep(unsigned q_val, unsigned n) noexcept : q(q_val), next(n) { }

};

__declspec(align(16)) static const Step g_step_tbl[4][4] = {
{ Step(Right, 0u, 0u), Step(Top, 0u, 1u), Step(Top, 1u, 1u), Step(Left, 1u, 0u) },
{ Step(Left, 1u, 1u), Step(Down, 1u, 0u), Step(Down, 0u, 0u), Step(Right, 0u, 1u) },
{ Step(Down, 1u, 1u), Step(Left, 0u, 1u), Step(Left, 0u, 0u), Step(Top, 1u, 0u) },
{ Step(Top, 0u, 0u), Step(Right, 1u, 0u), Step(Right, 1u, 1u), Step(Down, 0u, 1u) }
};

__declspec(align(16)) static const InvStep g_inv_tbl[4][4] = {
{ InvStep(0u, Right), InvStep(1u, Top), InvStep(3u, Left), InvStep(2u, Top) },
{ InvStep(2u, Down), InvStep(3u, Right), InvStep(1u, Down), InvStep(0u, Left) },
{ InvStep(2u, Left), InvStep(1u, Left), InvStep(3u, Top), InvStep(0u, Down) },
{ InvStep(0u, Top), InvStep(3u, Down), InvStep(1u, Right), InvStep(2u, Right) }
};

static const boost::mpi::environment* g_env;
static const boost::mpi::communicator* g_world;
static const pybind11::scoped_interpreter* g_pyInterpreter;
static const pybind11::module_* g_pyOptimizerBridge;
static std::string g_exeDirCache;

__declspec(align(16)) struct CrossMsg final {
float s_x1;
float s_x2;
float e_x1;
float e_x2;
float Rtop;

text
template <typename Archive> __declspec(noalias) __forceinline void serialize(Archive& ar, unsigned int) noexcept { ar& s_x1& s_x2& e_x1& e_x2& Rtop; }

};

__declspec(align(16)) struct MultiCrossMsg final {
float intervals[35];
unsigned count;

text
template <typename Archive> __declspec(noalias) __forceinline void serialize(Archive& ar, unsigned int) noexcept { ar& intervals& count; }

};

__declspec(align(16)) struct BestSolutionMsg final {
float bestF;
float bestX;
float bestY;
float bestQ[32];
float bestI[8];
unsigned dim;

text
template <typename Archive> __declspec(noalias) __forceinline void serialize(Archive& ar, unsigned int) noexcept { ar& bestF& bestX& bestY& bestQ& bestI& dim; }

};

__declspec(align(16)) struct Slab final sealed{
char* const base;
char* current;
char* const end;

text
__forceinline Slab(void* memory, size_t usable) noexcept : base(static_cast<char*>(memory)), current(base), end(base + (usable & ~static_cast<size_t>(63u))) {

}
};

static thread_local tbb::enumerable_thread_specific<Slab*> tls( noexcept {
void* memory = _aligned_malloc(16777216u, 16u);
Slab* slab = static_cast<Slab*>(_aligned_malloc(sizeof(Slab), 16u));
new (slab) Slab(memory, 16777216u);
char* p = slab->base;
#pragma loop ivdep
while (p < slab->end) {
*p = 0;
p += 4096u;
}
return slab;
});

__declspec(align(16)) struct Peano2DMap final sealed{
const int levels;
const float a;
const float b;
const float c;
const float d;
const float lenx;
const float leny;
const float inv_lenx;
const unsigned scale;
const unsigned start;

text
__forceinline Peano2DMap(int L, float _a, float _b, float _c, float _d, unsigned st) noexcept : levels(L), a(_a), b(_b), c(_c), d(_d), lenx(_b - _a), leny(_d - _c), inv_lenx(1.0f / (_b - _a)), scale(static_cast<unsigned>(1u) << (L << 1)), start(st) {

}
};

static Peano2DMap gActiveMap(0, 0, 0, 0, 0, 0);

__declspec(align(16)) struct Interval1D final sealed{
const float x1;
const float x2;
const float y1;
const float y2;
const float delta_y;
const float ordinate_factor;
const float N_factor;
const float quadratic_term;
const float M;
float R;

text
static __declspec(noalias) __forceinline void* operator new(size_t) noexcept { Slab* s = tls.local(); char* r = s->current; s->current += 64u; return r; } __declspec(noalias) __forceinline Interval1D(float _x1, float _x2, float _y1, float _y2, float _N) noexcept : x1(_x1), x2(_x2), y1(_y1), y2(_y2), delta_y(_y2 - _y1), ordinate_factor(-(y1 + y2) * 2.0f), N_factor(_N == 1.0f ? _x2 - _x1 : sqrtf(_x2 - _x1)), quadratic_term(fmaf((1.0f / N_factor)* delta_y, delta_y, 0.0f)), M(fabsf(delta_y) / N_factor) {

}

__declspec(noalias) __forceinline void ChangeCharacteristic(float _m) noexcept {
R = fmaf(1.0f / _m, quadratic_term, fmaf(_m, N_factor, ordinate_factor));
}
};

static __declspec(noalias) __forceinline bool ComparePtr1D(const Interval1D* a, const Interval1D* b) noexcept {
return a->R < b->R;
}

static __declspec(noalias) __forceinline void RecomputeR_ConstM_AVX2_1D(Interval1D* const* arr, size_t n, float m) noexcept {
const __m256 vm = _mm256_set1_ps(m);
__m256 vinvm = _mm256_rcp_ps(vm);
vinvm = _mm256_mul_ps(vinvm, _mm256_fnmadd_ps(vm, vinvm, _mm256_set1_ps(2.0f)));
size_t i = 0;
size_t limit = n & ~static_cast<size_t>(7ull);
alignas(16) float q[8];
alignas(16) float nf[8];
alignas(16) float od[8];
alignas(16) float out[8];
#pragma loop ivdep
while (i < limit) {
int k = 0;
#pragma loop ivdep
while (k < 8) {
const Interval1D* p = arr[i + k];
q[k] = p->quadratic_term;
nf[k] = p->N_factor;
od[k] = p->ordinate_factor;
++k;
}
const __m256 vq = _mm256_load_ps(q);
const __m256 vnf = _mm256_load_ps(nf);
const __m256 vod = _mm256_load_ps(od);
const __m256 t = _mm256_fmadd_ps(vm, vnf, vod);
const __m256 res = _mm256_fmadd_ps(vq, vinvm, t);
_mm256_store_ps(out, res);
k = 0;
#pragma loop ivdep
while (k < 8) {
arr[i + k]->R = out[k];
++k;
}
i += 8;
}
while (i < n) {
arr[i]->ChangeCharacteristic(m);
++i;
}
}

static __declspec(noalias) __forceinline void RecomputeR_AffineM_AVX2_1D(Interval1D* const* arr, size_t n, float GF, float alpha) noexcept {
const __m256 vGF = _mm256_set1_ps(GF);
const __m256 va = _mm256_set1_ps(alpha);
size_t i = 0;
size_t limit = n & ~static_cast<size_t>(7ull);
alignas(16) float ln[8];
alignas(16) float Mv[8];
alignas(16) float q[8];
alignas(16) float nf[8];
alignas(16) float od[8];
alignas(16) float out[8];
#pragma loop ivdep
while (i < limit) {
int k = 0;
#pragma loop ivdep
while (k < 8) {
const Interval1D* p = arr[i + k];
ln[k] = p->x2 - p->x1;
Mv[k] = p->M;
q[k] = p->quadratic_term;
nf[k] = p->N_factor;
od[k] = p->ordinate_factor;
++k;
}
const __m256 vln = _mm256_load_ps(ln);
const __m256 vM = _mm256_load_ps(Mv);
const __m256 vq = _mm256_load_ps(q);
const __m256 vnf = _mm256_load_ps(nf);
const __m256 vod = _mm256_load_ps(od);
const __m256 vm = _mm256_fmadd_ps(vGF, vln, _mm256_mul_ps(va, vM));
__m256 vinvm = _mm256_rcp_ps(vm);
vinvm = _mm256_mul_ps(vinvm, _mm256_fnmadd_ps(vm, vinvm, _mm256_set1_ps(2.0f)));
const __m256 t = _mm256_fmadd_ps(vm, vnf, vod);
const __m256 res = _mm256_fmadd_ps(vq, vinvm, t);
_mm256_store_ps(out, res);
k = 0;
#pragma loop ivdep
while (k < 8) {
arr[i + k]->R = out[k];
++k;
}
i += 8;
}
while (i < n) {
const Interval1D* p = arr[i];
const float mi = fmaf(GF, fmaf(-p->x1, 1.0f, p->x2), fmaf(p->M, alpha, 0.0f));
arr[i]->R = fmaf(1.0f / mi, p->quadratic_term, fmaf(mi, p->N_factor, p->ordinate_factor));
++i;
}
}

__declspec(align(16)) struct IntervalND final sealed{
const float x1;
const float x2;
const float y1;
const float y2;
const float delta_y;
const float ordinate_factor;
float N_factor;
float quadratic_term;
float M;
float R;
unsigned long long i1;
unsigned long long i2;
float diam;
int span_level;

text
static __declspec(noalias) __forceinline void* operator new(size_t) noexcept { Slab* s = tls.local(); char* r = s->current; s->current += 64u; return r; } __declspec(noalias) __forceinline IntervalND(float _x1, float _x2, float _y1, float _y2) noexcept : x1(_x1), x2(_x2), y1(_y1), y2(_y2), delta_y(fmaf(_y2, 1.0f, -_y1)), ordinate_factor(fmaf(fmaf(-y1, 1.0f, -y2), 2.0f, 0.0f)), N_factor(0), quadratic_term(0), M(0), R(0), i1(0), i2(0), diam(0), span_level(0) {

}

__declspec(noalias) __forceinline void compute_span_level(const struct MortonND& map) noexcept;
__declspec(noalias) __forceinline void set_metric(float d_alpha) noexcept {
N_factor = d_alpha;
quadratic_term = fmaf(1.0f / N_factor, fmaf(delta_y, delta_y, 0.0f), 0.0f);
M = fmaf(1.0f / N_factor, fabsf(delta_y), 0.0f);
}
__declspec(noalias) __forceinline void ChangeCharacteristic(float _m) noexcept {
R = fmaf(1.0f / _m, quadratic_term, fmaf(_m, N_factor, ordinate_factor));
}
};

static __declspec(noalias) __forceinline bool ComparePtrND(const IntervalND* a, const IntervalND* b) noexcept {
return a->R < b->R;
}

static __declspec(noalias) __forceinline void RecomputeR_ConstM_AVX2_ND(IntervalND* const* arr, size_t n, float m) noexcept {
const __m256 vm = _mm256_set1_ps(m);
__m256 vinvm = _mm256_rcp_ps(vm);
vinvm = _mm256_mul_ps(vinvm, _mm256_fnmadd_ps(vm, vinvm, _mm256_set1_ps(2.0f)));
size_t i = 0;
size_t limit = n & ~static_cast<size_t>(7ull);
alignas(16) float q[8];
alignas(16) float nf[8];
alignas(16) float od[8];
alignas(16) float out[8];
#pragma loop ivdep
while (i < limit) {
int k = 0;
#pragma loop ivdep
while (k < 8) {
const IntervalND* p = arr[i + k];
q[k] = p->quadratic_term;
nf[k] = p->N_factor;
od[k] = p->ordinate_factor;
++k;
}
const __m256 vq = _mm256_load_ps(q);
const __m256 vnf = _mm256_load_ps(nf);
const __m256 vod = _mm256_load_ps(od);
const __m256 t = _mm256_fmadd_ps(vm, vnf, vod);
const __m256 res = _mm256_fmadd_ps(vq, vinvm, t);
_mm256_store_ps(out, res);
k = 0;
#pragma loop ivdep
while (k < 8) {
arr[i + k]->R = out[k];
++k;
}
i += 8;
}
while (i < n) {
arr[i]->ChangeCharacteristic(m);
++i;
}
}

static __declspec(noalias) __forceinline void RecomputeR_AffineM_AVX2_ND(IntervalND* const* arr, size_t n, float GF, float alpha) noexcept {
const __m256 vGF = _mm256_set1_ps(GF);
const __m256 va = _mm256_set1_ps(alpha);
size_t i = 0;
size_t limit = n & ~static_cast<size_t>(7ull);
alignas(16) float ln[8];
alignas(16) float Mv[8];
alignas(16) float q[8];
alignas(16) float nf[8];
alignas(16) float od[8];
alignas(16) float out[8];
#pragma loop ivdep
while (i < limit) {
int k = 0;
#pragma loop ivdep
while (k < 8) {
const IntervalND* p = arr[i + k];
ln[k] = p->x2 - p->x1;
Mv[k] = p->M;
q[k] = p->quadratic_term;
nf[k] = p->N_factor;
od[k] = p->ordinate_factor;
++k;
}
const __m256 vln = _mm256_load_ps(ln);
const __m256 vM = _mm256_load_ps(Mv);
const __m256 vq = _mm256_load_ps(q);
const __m256 vnf = _mm256_load_ps(nf);
const __m256 vod = _mm256_load_ps(od);
const __m256 vm = _mm256_fmadd_ps(vGF, vln, _mm256_mul_ps(va, vM));
__m256 vinvm = _mm256_rcp_ps(vm);
vinvm = _mm256_mul_ps(vinvm, _mm256_fnmadd_ps(vm, vinvm, _mm256_set1_ps(2.0f)));
const __m256 t = _mm256_fmadd_ps(vm, vnf, vod);
const __m256 res = _mm256_fmadd_ps(vq, vinvm, t);
_mm256_store_ps(out, res);
k = 0;
#pragma loop ivdep
while (k < 8) {
arr[i + k]->R = out[k];
++k;
}
i += 8;
}
while (i < n) {
const IntervalND* p = arr[i];
const float mi = fmaf(GF, fmaf(-p->x1, 1.0f, p->x2), fmaf(p->M, alpha, 0.0f));
arr[i]->R = fmaf(1.0f / mi, p->quadratic_term, fmaf(mi, p->N_factor, p->ordinate_factor));
++i;
}
}

static __declspec(noalias) __forceinline float fast_pow_int(const float v, const int n) noexcept {
float r;
switch (n) {
case 3: {
const float v2 = fmaf(v, v, 0.0f);
r = fmaf(v2, v, 0.0f);
} break;
case 4: {
const float v2 = fmaf(v, v, 0.0f);
r = fmaf(v2, v2, 0.0f);
} break;
case 5: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
r = fmaf(fmaf(v4, v2, 0.0f), v, 0.0f);
} break;
case 6: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
r = fmaf(v4, v2, 0.0f);
} break;
case 7: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
r = fmaf(fmaf(v4, v2, 0.0f), v, 0.0f);
} break;
case 8: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
r = fmaf(v4, v4, 0.0f);
} break;
case 9: {
const float v3 = fmaf(fmaf(v, v, 0.0f), v, 0.0f);
const float v6 = fmaf(v3, v3, 0.0f);
r = fmaf(v6, v3, 0.0f);
} break;
case 10: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
const float v8 = fmaf(v4, v4, 0.0f);
r = fmaf(v8, v2, 0.0f);
} break;
case 11: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
const float v8 = fmaf(v4, v4, 0.0f);
r = fmaf(fmaf(v8, v2, 0.0f), v, 0.0f);
} break;
case 12: {
const float v3 = fmaf(fmaf(v, v, 0.0f), v, 0.0f);
const float v6 = fmaf(v3, v3, 0.0f);
r = fmaf(v6, v6, 0.0f);
} break;
case 13: {
const float v3 = fmaf(fmaf(v, v, 0.0f), v, 0.0f);
const float v6 = fmaf(v3, v3, 0.0f);
r = fmaf(fmaf(v6, v6, 0.0f), v, 0.0f);
} break;
case 14: {
const float v7 = fmaf(fmaf(fmaf(fmaf(fmaf(fmaf(v, v, 0.0f), v, 0.0f), v, 0.0f), v, 0.0f), v, 0.0f), v, 0.0f);
r = fmaf(v7, v7, 0.0f);
} break;
case 15: {
const float v7 = fmaf(fmaf(fmaf(fmaf(fmaf(fmaf(v, v, 0.0f), v, 0.0f), v, 0.0f), v, 0.0f), v, 0.0f), v, 0.0f);
r = fmaf(fmaf(v7, v7, 0.0f), v, 0.0f);
} break;
default: {
const float v2 = fmaf(v, v, 0.0f);
const float v4 = fmaf(v2, v2, 0.0f);
const float v8 = fmaf(v4, v4, 0.0f);
r = fmaf(v8, v8, 0.0f);
}
}
return r;
}

static __declspec(noalias) __forceinline float step(const float _m, const float x1, const float x2, const float y1, const float y2, const float _N, const float _r) noexcept {
const float diff = fmaf(y2, 1.0f, -y1);
const unsigned sign_mask = ((reinterpret_cast<const unsigned>(&diff)) & 0x80000000u) ^ 0x80000000u;
const float sign_mult = reinterpret_cast<const float>(&sign_mask);
if (_N == 1.0f)
return fmaf(fmaf(-(1.0f / _m), diff, x1 + x2), 0.5f, 0.0f);
if (_N == 2.0f)
return fmaf(fmaf(fmaf(fmaf(1.0f / fmaf(_m, _m, 0.0f), sign_mult, 0.0f), fmaf(fmaf(diff, diff, 0.0f), _r, 0.0f), 0.0f), 1.0f, fmaf(x1, 1.0f, x2)), 0.5f, 0.0f);
return fmaf(fmaf(fmaf(fmaf(1.0f / fast_pow_int(_m, static_cast<int>(_N)), sign_mult, 0.0f), fast_pow_int(fabsf(diff), static_cast<int>(_N)), 0.0f), _r, 0.0f), 1.0f, fmaf(x1, 1.0f, x2)) * 0.5f;
}

__declspec(align(16)) struct MortonCachePerRank final sealed{
std::vector<int> permCache;
std::vector<unsigned long long> invMaskCache;
unsigned baseSeed;
};

static thread_local MortonCachePerRank g_mc;

static __declspec(noalias) __forceinline unsigned long long gray_encode(unsigned long long x) noexcept {
return x ^ (x >> 1);
}

static __declspec(noalias) __forceinline long long gray_decode(unsigned long long g) noexcept {
g ^= g >> 32;
g ^= g >> 16;
g ^= g >> 8;
g ^= g >> 4;
g ^= g >> 2;
g ^= g >> 1;
return static_cast<long long>(g);
}

__declspec(align(16)) struct MortonND final sealed{
const int dim;
const int levels;
const int eff_levels;
const int extra_levels;
const int chunks;
std::vector<int> chunk_bits;
std::vector<unsigned long long> chunk_bases;
unsigned long long scale;
std::vector<float> low;
std::vector<float> high;
std::vector<float> step;
std::vector<float> invStep;
std::vector<float> baseOff;
std::vector<int> perm;
std::vector<unsigned long long> invMask;
std::vector<unsigned long long> pextMask;
std::vector<unsigned long long> pextMaskChunks;
const float invScaleLevel;
const bool use_gray;

text
static __declspec(noalias) __forceinline unsigned long long make_mask(int dim, int Lc, int d) noexcept { unsigned long long m = 0ull; unsigned long long bitpos = static_cast<unsigned long long>(d); int b = 0;

#pragma loop ivdep
while (b < Lc) {
m |= 1ull << bitpos;
bitpos += static_cast<unsigned long long>(dim);
++b;
}
return m;
}

text
__declspec(noalias) __forceinline MortonND(int D, int L, const float* lows, const float* highs, const MortonCachePerRank& mc) : dim(D), levels(L), eff_levels((std::max)(1, static_cast<int>(63 / (D ? D : 1)))), extra_levels((L > eff_levels) ? (L - eff_levels) : 0), chunks((extra_levels > 0) ? (1 + (extra_levels + eff_levels - 1) / eff_levels) : 1), low(lows, lows + D), high(highs, highs + D), step(D, 0.0f), invStep(D, 0.0f), baseOff(D, 0.0f), perm(mc.permCache.begin(), mc.permCache.begin() + D), invMask(mc.invMaskCache.begin(), mc.invMaskCache.begin() + D), invScaleLevel(1.0f / static_cast<float>(static_cast<unsigned long long>(1) << L)), use_gray(true) { int d = 0;

#pragma loop ivdep
while (d < dim) {
const float rng = high[d] - low[d];
const float st = rng * invScaleLevel;
step[d] = st;
invStep[d] = 1.0f / st;
baseOff[d] = fmaf(0.5f, st, low[d]);
++d;
}
chunk_bits.resize(chunks);
pextMaskChunks.resize(static_cast<size_t>(chunks) * static_cast<size_t>(dim));
chunk_bases.resize(chunks);
int remaining = levels;
int c = 0;
while (c < chunks) {
const int Lc = (c == 0) ? (std::min)(eff_levels, remaining) : (std::min)(eff_levels, remaining);
chunk_bits[c] = Lc;
remaining -= Lc;
const unsigned long long baseC = static_cast<unsigned long long>(1) << (dim * Lc);
chunk_bases[c] = baseC;
d = 0;
#pragma loop ivdep
while (d < dim) {
pextMaskChunks[static_cast<size_t>(c) * static_cast<size_t>(dim) + static_cast<size_t>(d)] = make_mask(dim, Lc, d);
++d;
}
++c;
}
pextMask.resize(dim);
d = 0;
#pragma loop ivdep
while (d < dim) {
pextMask[d] = make_mask(dim, chunk_bits[0], d);
++d;
}
scale = static_cast<unsigned long long>(1) << (dim * chunk_bits[0]);
}

text
__declspec(noalias) __forceinline float block_diameter(unsigned long long i1, unsigned long long i2) const noexcept { if (i1 > i2) std::swap(i1, i2); float s2 = 0.0f; int d = 0;

#pragma loop ivdep
while (d < dim) {
const int pd = perm[d];
const unsigned long long varying = (i1 ^ i2) & pextMask[d];
const int nfree_hi = _mm_popcnt_u64(varying);
const int nfree_total = nfree_hi + levels - chunk_bits[0];
const float range = fmaf(step[pd], fmaf(ldexpf(1.0f, nfree_total), 1.0f, -1.0f), 0.0f);
s2 = fmaf(range, range, s2);
++d;
}
return sqrtf(s2);
}

text
__declspec(noalias) __forceinline void map01ToPoint(float t, float* __restrict out) const noexcept { unsigned long long accBits[32] = {0ull}; int accShifted[32] = {0}; int c = 0; while (c < chunks) { const int Lc = chunk_bits[c]; const unsigned long long baseC = chunk_bases[c]; const float scaled = t * static_cast<float>(baseC); unsigned long long idxc = (scaled >= static_cast<float>(baseC)) ? (baseC - 1ull) : static_cast<unsigned long long>(scaled); if (use_gray) idxc = gray_encode(idxc); int shift_from_top = 0; int k = 0; while (k <= c) { shift_from_top += chunk_bits[k]; ++k; } const int inv_shift = levels - shift_from_top; int d = 0;

#pragma loop ivdep
while (d < dim) {
const int pd = perm[d];
const unsigned long long mask = pextMaskChunks[static_cast<size_t>(c) * static_cast<size_t>(dim) + static_cast<size_t>(d)];
unsigned long long bits = _pext_u64(idxc, mask);
if (inv_shift >= 0) {
unsigned long long invMaskSegment = 0ull;
if (chunk_bits[c] < 63) {
const unsigned long long take = (static_cast<unsigned long long>(1) << chunk_bits[c]) - 1ull;
invMaskSegment = (invMask[pd] >> inv_shift) & take;
}
bits ^= invMaskSegment;
}
accBits[pd] = (accBits[pd] << Lc) | bits;
accShifted[pd] += Lc;
++d;
}
++c;
}
int d = 0;
#pragma loop ivdep
while (d < dim) {
out[d] = fmaf(step[d], static_cast<float>(accBits[d]), baseOff[d]);
++d;
}
}

text
__declspec(noalias) __forceinline float pointToT(const float* __restrict q) const noexcept { const int bitsFull = levels; const int bitsCoarse = chunk_bits[0]; unsigned long long idx0 = 0ull; int d = 0;

#pragma loop ivdep
while (d < dim) {
const int pd = perm[d];
const float v = (q[pd] - baseOff[pd]) * invStep[pd];
const long long cell = static_cast<long long>(_mm_cvt_ss2si(_mm_round_ss(_mm_setzero_ps(), _mm_set_ss(v), _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC)));
const long long maxv = (static_cast<long long>(1) << bitsFull) - 1;
(void)maxv;
unsigned long long b = static_cast<unsigned long long>(cell) >> (bitsFull - bitsCoarse);
unsigned long long invMask0 = 0ull;
if (bitsCoarse < 63) {
const unsigned long long take = (static_cast<unsigned long long>(1) << bitsCoarse) - 1ull;
invMask0 = (invMask[pd] >> (bitsFull - bitsCoarse)) & take;
}
b ^= invMask0;
idx0 |= _pdep_u64(b, pextMask[d]);
++d;
}
if (use_gray)
idx0 = static_cast<unsigned long long>(gray_decode(idx0));
return fmaf(1.0f / static_cast<float>(scale), fmaf(static_cast<float>(idx0), 1.0f, 0.5f), 0.0f);
}
};

__declspec(noalias) __forceinline void IntervalND::compute_span_level(const MortonND& map) noexcept {
span_level = 0;
int d = 0;
#pragma loop ivdep
while (d < map.dim) {
const unsigned long long varying = (i1 ^ i2) & map.pextMask[d];
span_level += _mm_popcnt_u64(varying);
++d;
}
span_level += (map.levels - map.chunk_bits[0]) * map.dim;
span_level = (std::min)(span_level, 11);
}

__declspec(align(16)) struct ManipCost final sealed{
const int n;
const bool variableLen;
const float targetX;
const float targetY;
const float minTheta;
const float archBiasW;
const float archBiasK;
const float sharpW;

text
__declspec(noalias) __forceinline ManipCost(int _n, bool _variableLen, float _targetX, float _targetY, float _minTheta) noexcept : n(_n), variableLen(_variableLen), targetX(_targetX), targetY(_targetY), minTheta(_minTheta), archBiasW(0.02f), archBiasK(3.0f), sharpW(0.05f) {

}

__declspec(noalias) __forceinline float operator()(const float* __restrict q, float& out_x, float& out_y) const noexcept {
const float* __restrict th = q;
const float* __restrict L = variableLen ? (q + n) : nullptr;
alignas(16) float phi[32];
alignas(16) float s_arr[32];
alignas(16) float c_arr[32];
float x = 0.0f;
float y = 0.0f;
float phi_acc = 0.0f;
float penC = 0.0f;
float archPen = 0.0f;
int i = 0;
#pragma loop ivdep
while (i < n) {
phi_acc += th[i];
phi[i] = phi_acc;
++i;
}
FABE13_SINCOS(phi, s_arr, c_arr, n);
const float sharpScale = 2.0f / (minTheta);
const float Lc = 1.0f;
if (variableLen) {
i = 0;
while (i < n) {
const float Li = L[i];
x = fmaf(Li, c_arr[i], x);
y = fmaf(Li, s_arr[i], y);
++i;
}
}
else {
i = 0;
while (i < n) {
x = fmaf(Lc, c_arr[i], x);
y = fmaf(Lc, s_arr[i], y);
++i;
}
}
i = 0;
#pragma loop ivdep
while (i < n) {
const float theta = th[i];
const float ai = fabsf(theta);
const float v = fmaf(ai, 1.0f, -minTheta);
if (v > 0.0f) {
const float scale = fmaf(sharpScale, v, 0.0f);
const float arg = fmaf(scale, 0.69314718055994530941723212145818f, 0.0f);
const float exp2_val = fmaf(arg, fmaf(arg, fmaf(arg, fmaf(arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
penC = fmaf(sharpW, fmaf(exp2_val, 1.0f, -1.0f), penC);
}
const float t = fmaf(-theta, archBiasK, 0.0f);
float sp;
if (t > 10.0f)
sp = t;
else {
const float exp_val = fmaf(t, fmaf(t, fmaf(t, fmaf(t, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
sp = log1pf(exp_val);
}
archPen = fmaf(archBiasW, sp, archPen);
++i;
}
const float dx = fmaf(x, 1.0f, -targetX);
const float dy = fmaf(y, 1.0f, -targetY);
const float dist = sqrtf(fmaf(dx, dx, dy * dy));
out_x = x;
out_y = y;
return fmaf(dist, 1.0f, fmaf(penC, 1.0f, archPen));
}
};

static __declspec(noalias) __forceinline void HitTest2D_analytic(float x_param, float& out_x1, float& out_x2) noexcept {
const float a = gActiveMap.a;
const float inv_lenx = gActiveMap.inv_lenx;
const unsigned scale = gActiveMap.scale;
const unsigned scale_minus_1 = scale - 1u;
const float lenx = gActiveMap.lenx;
const float leny = gActiveMap.leny;
const float c = gActiveMap.c;
const unsigned start = gActiveMap.start;
const int levels = gActiveMap.levels;
float norm = (x_param - a) * inv_lenx;
norm = fminf(fmaxf(norm, 0.0f), 0x1.fffffep-1f);
unsigned idx = static_cast<unsigned>(norm * static_cast<float>(scale));
idx = idx > scale_minus_1 ? scale_minus_1 : idx;
float sx = lenx;
float sy = leny;
float x1 = a;
float x2 = c;
unsigned type = start;
int l = levels - 1;
while (l >= 0) {
const unsigned q = (idx >> (l * 2)) & 3u;
const Step s = g_step_tbl[type][q];
type = s.next;
sx *= 0.5f;
sy *= 0.5f;
x1 += s.dx ? sx : 0.0f;
x2 += s.dy ? sy : 0.0f;
--l;
}
out_x1 = x1 + sx * 0.5f;
out_x2 = x2 + sy * 0.5f;
}

static __declspec(noalias) __forceinline float FindX2D_analytic(float px, float py) noexcept {
const float a = gActiveMap.a;
const float b = gActiveMap.b;
const float c = gActiveMap.c;
const float d = gActiveMap.d;
const float lenx = gActiveMap.lenx;
const float leny = gActiveMap.leny;
const unsigned scale = gActiveMap.scale;
const unsigned start = gActiveMap.start;
const int levels = gActiveMap.levels;
const float clamped_px = fminf(fmaxf(px, a), b);
const float clamped_py = fminf(fmaxf(py, c), d);
float sx = lenx;
float sy = leny;
float x0 = a;
float y0 = c;
unsigned idx = 0u;
unsigned type = start;
int l = 0;
while (l < levels) {
sx *= 0.5f;
sy *= 0.5f;
const float mx = x0 + sx;
const float my = y0 + sy;
const unsigned tr = static_cast<unsigned>((clamped_px > mx) & (clamped_py > my));
const unsigned tl = static_cast<unsigned>((clamped_px < mx) & (clamped_py > my));
const unsigned dl = static_cast<unsigned>((clamped_px < mx) & (clamped_py < my));
const unsigned none = static_cast<unsigned>(1u ^ (tr | tl | dl));
const unsigned dd = (tr << 1) | tr | tl | (none << 1);
const InvStep inv = g_inv_tbl[type][dd];
type = inv.next;
idx = (idx << 2) | inv.q;
const unsigned dx = dd >> 1;
const unsigned dy = dd & 1u;
x0 += dx ? sx : 0.0f;
y0 += dy ? sy : 0.0f;
++l;
}
const float scale_recip = 1.0f / static_cast<float>(scale);
return fmaf(static_cast<float>(idx) * scale_recip, lenx, a);
}

static __declspec(noalias) __forceinline int generate_lhs_seeds_lite(const MortonND& map, int dim, float* __restrict S, int stride, unsigned seed) noexcept {
int temp_dim = dim;
const int ns = static_cast<int>(fmaf(static_cast<float>(--temp_dim), fmaf(static_cast<float>(temp_dim), fmaf(static_cast<float>(temp_dim), fmaf(static_cast<float>(temp_dim), 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f));
unsigned st = seed;
alignas(16) int permutations[32][256];
int d = 0;
#pragma loop ivdep
while (d < dim) {
int s = 0;
#pragma loop ivdep
while (s < ns) {
permutations[d][s] = s;
++s;
}
s = ns - 1;
while (s > 0) {
st ^= st << 13;
st ^= st >> 17;
st ^= st << 5;
const int j = static_cast<int>(st % static_cast<unsigned>(s + 1));
const int t = permutations[d][s];
permutations[d][s] = permutations[d][j];
permutations[d][j] = t;
--s;
}
++d;
}
int s2 = 0;
#pragma loop ivdep
while (s2 < ns) {
d = 0;
#pragma loop ivdep
while (d < dim) {
st ^= st << 13;
st ^= st >> 17;
st ^= st << 5;
const float u = (st & 0xFFFFFF) * 5.9604645e-8f;
const int stratum = permutations[d][s2];
const float pos = (static_cast<float>(stratum) + u) / static_cast<float>(ns);
const int pd = map.perm[d];
const float lo = map.low[pd];
const float hi = map.high[pd];
S[s2 * stride + d] = fmaf(pos, hi - lo, lo);
++d;
}
++s2;
}
return ns;
}

static __declspec(noalias) __forceinline int generate_heuristic_seeds(const ManipCost& cost, const MortonND& map, int dim, float* __restrict S, int stride, unsigned seed) noexcept {
const int n = cost.n;
const bool VL = cost.variableLen;
const float tx = cost.targetX;
const float ty = cost.targetY;
int total_seeds = 0;
{
float* s0 = S + total_seeds * stride;
float sin_phi;
float cos_phi;
const float rho = sqrtf(fmaf(tx, tx, ty * ty));
FABE13_SINCOS(&tx, &sin_phi, &cos_phi, 1);
const float phi = (fabsf(sin_phi) > 0.0f) ? atan2f(ty, tx) : 0.0f;
const float len = fmaf(1.0f / static_cast<float>(n), rho, 0.0f);
int i = 0;
#pragma loop ivdep
while (i < n) {
s0[i] = (1.0f / static_cast<float>(n)) * phi;
++i;
}
if (VL) {
i = 0;
while (i < n) {
s0[n + i] = len;
++i;
}
}
++total_seeds;
}
{
float* s1 = S + total_seeds * stride;
float sin_phi;
float cos_phi;
FABE13_SINCOS(&tx, &sin_phi, &cos_phi, 1);
const float phi = (fabsf(sin_phi) > 0.0f) ? atan2f(ty, tx) : 0.0f;
int i = 0;
#pragma loop ivdep
while (i < n) {
s1[i] = 0.5f * phi * ((i & 1) ? -1.0f : 1.0f);
++i;
}
if (VL) {
i = 0;
while (i < n) {
s1[n + i] = fmaf(0.4f, static_cast<float>(i) / static_cast<float>(n), 0.8f);
++i;
}
}
++total_seeds;
}
{
float* s2 = S + total_seeds * stride;
const float inv = (n > 1) ? 1.0f / static_cast<float>(n - 1) : 0.0f;
float sin_phi;
float cos_phi;
FABE13_SINCOS(&tx, &sin_phi, &cos_phi, 1);
const float phi = (fabsf(sin_phi) > 0.0f) ? atan2f(ty, tx) : 0.0f;
int i = 0;
#pragma loop ivdep
while (i < n) {
const float pr = static_cast<float>(i) * inv;
s2[i] = fmaf(phi, fmaf(-0.3f, pr, 1.0f), 0.0f);
++i;
}
if (VL) {
int j = 0;
while (j < n) {
float si;
FABE13_SIN(fmaf(1.5f, static_cast<float>(j), 0.0f), si);
s2[n + j] = fmaf(0.2f, si, 1.0f);
++j;
}
}
++total_seeds;
}
const int lhs_count = generate_lhs_seeds_lite(map, dim, S + total_seeds * stride, stride, seed);
total_seeds += lhs_count;
return total_seeds;
}

static declspec(noalias) void agp_run_branch_mpi(
const MortonND& map,
const ManipCost& cost,
int maxIter,
float r,
bool adaptive,
float eps,
unsigned seed,
std::vector<IntervalND*, boost::alignment::aligned_allocator<IntervalND*, 16u>>& H,
std::vector<float, boost::alignment::aligned_allocator<float, 16u>>& bestQ,
float& bestF,
float& bestX,
float& bestY,
size_t& out_iterations,
float& out_achieved_epsilon,
float M_prior) noexcept {
const int n = cost.n;
const int dim = n + (cost.variableLen ? n : 0);
const float dim_f = static_cast<float>(dim);
unsigned exchange_counter = 0;
unsigned exchange_counter_T = 0;
alignas(16) float M_by_span[12];
int msi = 0;
while (msi < 12)
M_by_span[msi++] = M_prior;
float Mmax = M_prior;
alignas(16) float q_local[32];
alignas(16) float phi[32];
alignas(16) float s_arr[32];
alignas(16) float c_arr[32];
alignas(16) float sum_s[32];
alignas(16) float sum_c[32];
alignas(16) float q_try[32];
bestQ.reserve(static_cast<size_t>(dim));
float x = 0.0f;
float y = 0.0f;
int no_improve = 0;
auto t_to_idx = [&](float t) -> unsigned long long {
unsigned long long idx = static_cast<unsigned long long>(fmaf(t, static_cast<float>(map.scale), 0.0f));
return idx;
};
auto update_pockets_and_Mmax = [&](IntervalND* I) {
const int k = I->span_level;
if (I->M > M_by_span[k])
M_by_span[k] = I->M;
if (M_by_span[k] > Mmax)
Mmax = M_by_span[k];
};
const float a = 0.0f;
const float b = 1.0f;
float p = 0.0f;
float dmax = b - a;
const float initial_len = dmax;
const float A_dim = fmaf(1.0f / sqrtf(dim_f + 6.75f), 5.535f, 0.0f);
const float A_dim
= fmaf(1.0f / sqrtf(dim_f + 7.0f), 3.485f, 0.0f);
const float A_dim
_ = fmaf(1.0f / sqrtf(dim_f + 6.75f), 3.425f, 0.0f);
const float B_dim = fmaf(A_dim, 0.7f, 0.0f);
const float B_dim_ = fmaf(A_dim_, 7.0f, 0.0f);
const float B_dim__ = fmaf(A_dim__, 4.325f, 0.0f);
const float log_argument = A_dim - 2.03f;
const float log_argument_ = A_dim_ - 1.9875f;
const float log_argument__ = A_dim__ - 2.03f;
const float C_dim = fmaf(log_argument, fmaf(log_argument, fmaf(log_argument, fmaf(log_argument, fmaf(log_argument, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), log_argument) - B_dim;
const float C_dim_ = fmaf(log_argument_, fmaf(log_argument_, fmaf(log_argument_, fmaf(log_argument_, fmaf(log_argument_, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), log_argument_) - B_dim_;
const float C_dim__ = fmaf(log_argument__, fmaf(log_argument__, fmaf(log_argument__, fmaf(log_argument__, fmaf(log_argument__, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), log_argument__) - B_dim__;
const float adaptive_coeff_addition = fmaf(C_dim, fmaf(C_dim, fmaf(C_dim, fmaf(C_dim, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
const float adaptive_coeff_addition_ = fmaf(C_dim_, fmaf(C_dim_, fmaf(C_dim_, fmaf(C_dim_, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
const float adaptive_coeff_addition__ = fmaf(C_dim__, fmaf(C_dim__, fmaf(C_dim__, fmaf(C_dim__, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
float adaptive_coeff = A_dim - adaptive_coeff_addition;
float adaptive_coeff_ = A_dim_ - adaptive_coeff_addition_;
float adaptive_coeff__ = A_dim__ - adaptive_coeff_addition__;
const float A_dim_clone = fmaf(A_dim - fmaf(-fmaf(B_dim, fmaf(B_dim, fmaf(B_dim, fmaf(B_dim, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), adaptive_coeff_addition, A_dim), 0.5f, 0.0f);
int it = 0;
auto evalAt = [&](const float t) -> float {
map.map01ToPoint(t, q_local);
float f = cost(q_local, x, y);
if (f < fmaf(bestF, adaptive_coeff, 0.0f)) {
float eta = fmaf(sqrtf(fmaf(-dim_f, 2.0f, 23.0f)), 0.0215f, 0.0f);
float acc = 0.0f;
int ii = 0;
while (ii < n) {
acc = fmaf(q_local[ii], 1.0f, acc);
phi[ii] = acc;
++ii;
}
FABE13_SINCOS(phi, s_arr, c_arr, n);
float as = 0.0f;
float ac = 0.0f;
int k = n - 1;
while (k >= 0) {
const float Lk = cost.variableLen ? q_local[n + k] : 1.0f;
as = fmaf(Lk, s_arr[k], as);
ac = fmaf(Lk, c_arr[k], ac);
sum_s[k] = as;
sum_c[k] = ac;
--k;
}
const float dx = fmaf(x, 1.0f, -cost.targetX);
const float dy = fmaf(y, 1.0f, -cost.targetY);
const float dist = sqrtf(fmaf(dx, dx, fmaf(dy, dy, 0.0f)));
const float inv_dist = 1.0f / dist;
int improve_step = 0;
int no_improve_step = 0;
int stepI = 0;
const float adaptive_window = fmaf(1.0f / fmaf(bestF, adaptive_coeff, 0.0f), fmaf(bestF, adaptive_coeff, -f), -1.0f);
const int threshold_iters_grad = static_cast<int>(fmaf(fmaf(adaptive_window, fmaf(adaptive_window, fmaf(adaptive_window, fmaf(adaptive_window, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), fmaf(sqrtf(dim_f), 5.775f, 0.0f), 0.0f));
while (stepI < threshold_iters_grad) {
int i = 0;
#pragma loop ivdep
while (i < n) {
float gpen = 0.0f;
{
const float ai = fabsf(q_local[i]);
const float v = fmaf(ai, 1.0f, -cost.minTheta);
if (v > 0.0f) {
const float scale_arg = fmaf(2.0f / (cost.minTheta), fmaf(v, 0.69314718055994530941723212145818f, 0.0f), 0.0f);
const float exp_val = fmaf(scale_arg, fmaf(scale_arg, fmaf(scale_arg, fmaf(scale_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
const float dpen_dtheta = fmaf(cost.sharpW, fmaf(exp_val, fmaf(1.0f / (cost.minTheta), 1.3862943611198906188344642429164f, 0.0f), 0.0f), copysignf(1.0f, q_local[i]));
gpen = fmaf(dpen_dtheta, 1.0f, gpen);
}
}
{
const float tsg = fmaf(-q_local[i], cost.archBiasK, 0.0f);
const float exp_arg = -tsg;
const float exp_val = fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f);
const float sig = 1.0f / fmaf(exp_val, 1.0f, 1.0f);
gpen = fmaf(-fmaf(cost.archBiasW, cost.archBiasK, 0.0f), sig, gpen);
}
const float g = fmaf(fmaf(dx, -sum_s[i], fmaf(dy, sum_c[i], 0.0f)), inv_dist, gpen);
q_try[i] = fmaf(-eta, g, q_local[i]);
const float lo = (i == 0) ? -1.0471975511965977461542144610932f : -2.6179938779914943653855361527329f;
const float hi = 2.6179938779914943653855361527329f;
if (q_try[i] < lo) q_try[i] = lo;
else if (q_try[i] > hi) q_try[i] = hi;
++i;
}
if (cost.variableLen) {
int j = 0;
#pragma loop ivdep
while (j < n) {
const float g = fmaf(fmaf(dx, c_arr[j], fmaf(dy, s_arr[j], 0.0f)), inv_dist, 0.0f);
float v = fmaf(-eta, g, q_local[n + j]);
if (v < 0.5f) v = 0.5f;
else if (v > 2.0f) v = 2.0f;
q_try[n + j] = v;
++j;
}
}
float x2;
float y2;
const float f2 = cost(q_try, x2, y2);
const float rel_improvement = fmaf(-1.0f / f, f2, 1.0f);
if (f2 < f) {
memcpy(q_local, q_try, static_cast<size_t>(dim) * sizeof(float));
++improve_step;
no_improve_step = 0;
f = f2;
x = x2;
y = y2;
if (improve_step > std::max(threshold_iters_grad >> 1, 2)) eta = dim > 2 ? fmaf(fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), rel_improvement), fmaf(1.0f / sqrtf(dim_f - 1.0f), 2.5285f, 0.0f), 1.0f) : eta = fmaf(fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), rel_improvement), 3.575f, 1.0f);
else { const float eta_arg = dim > 2 ? fmaf(1.0f / sqrtf(dim_f - 1.0f), fmaf(rel_improvement, 1.11375f, 0.0f), 0.0f) : fmaf(rel_improvement, 1.575f, 0.0f); eta = fmaf(eta_arg, fmaf(eta_arg, fmaf(eta_arg, fmaf(eta_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f); }
}
else {
memcpy(q_try, q_local, static_cast<size_t>(dim) * sizeof(float));
++no_improve_step;
improve_step = 0;
if (no_improve_step > std::max(threshold_iters_grad >> 2, 2)) { const float eta_arg = fmaf(sqrtf(dim_f), fmaf(rel_improvement, 4.5f, 0.0f), 0.0f); eta = fmaf(eta_arg, fmaf(eta_arg, fmaf(eta_arg, fmaf(eta_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f); }
else eta = fmaf(fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, fmaf(rel_improvement, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), rel_improvement), fmaf(sqrtf(dim_f), 1.575f, 0.0f), 1.0f);
if (eta < 0.015f) break;
}
++stepI;
}
if (f < bestF) {
bestF = f;
bestQ.assign(q_local, q_local + dim);
bestX = x;
bestY = y;
no_improve = 0;
}
else ++no_improve;
return f;
}
};
const float f_a = evalAt(a);
const float f_b = evalAt(b);
const int K = static_cast<int>(fmaf(-fmaf(sqrtf(dim_f), dim_f, 0.0f), 0.725f, 10.95f));
H.reserve(static_cast<size_t>(maxIter) + static_cast<size_t>(K) + 16u);
const int rank = g_world->rank();
const int world = g_world->size();
alignas(16) float seeds[256 * 32];
const int seedCnt = generate_heuristic_seeds(cost, map, dim, seeds, 32, static_cast<unsigned>(fmaf(static_cast<float>(rank), 7919.0f, static_cast<float>(seed))));
int i = 0;
while (i < seedCnt) {
const float* s = seeds + static_cast<size_t>(fmaf(static_cast<float>(i), 32.0f, 0.0f));
const float t_seed = map.pointToT(s);
const float interval_size = (i < 3) ? fmaf(0.0004f, static_cast<float>(dim), 0.0f) : fmaf(fmaf(0.00031f, static_cast<float>(dim), 0.0f), exp2f((1.0f / static_cast<float>(seedCnt - 4)) * log2f(fmaf(0.00025f, 1.0f / 0.00031f, 0.0f)) * static_cast<float>(i - 3)), 0.0f);
const float t1 = fmaf(-interval_size, 1.0f, t_seed);
const float t2 = fmaf(interval_size, 1.0f, t_seed);
alignas(16) float q1[32];
alignas(16) float q2[32];
float x1;
float y1;
float x2;
float y2;
map.map01ToPoint(t1, q1);
const float f1 = cost(q1, x1, y1);
map.map01ToPoint(t2, q2);
const float f2 = cost(q2, x2, y2);
IntervalND* I = new IntervalND(t1, t2, f1, f2);
I->i1 = t_to_idx(t1);
I->i2 = t_to_idx(t2);
I->diam = map.block_diameter(I->i1, I->i2);
I->compute_span_level(map);
I->set_metric(I->diam);
update_pockets_and_Mmax(I);
I->ChangeCharacteristic(fmaf(r, Mmax, 0.0f));
if (i < 3)
I->R = fmaf(I->R, fmaf(0.01f, static_cast<float>(dim), 0.85f), 0.0f);
else {
const float start_mult = fmaf(0.214f, static_cast<float>(dim), 0.0f);
const float end_mult = fmaf(0.174f, static_cast<float>(dim), 0.0f);
const float mult = fmaf(start_mult, exp2f((1.0f / static_cast<float>(seedCnt - 4)) * log2f(fmaf(end_mult, 1.0f / start_mult, 0.0f)) * static_cast<float>(i - 3)), 0.0f);
I->R = fmaf(I->R, mult, 0.0f);
}
H.emplace_back(I);
std::push_heap(H.begin(), H.end(), ComparePtrND);
if (f1 < bestF) {
bestF = f1;
bestQ.assign(q1, q1 + dim);
bestX = x1;
bestY = y1;
}
if (f2 < bestF) {
bestF = f2;
bestQ.assign(q2, q2 + dim);
bestX = x2;
bestY = y2;
}
++i;
}
float prev_t = a;
float prev_f = f_a;
int k = 1;
while (k <= K) {
const float t = fmaf(fmaf(b - a, static_cast<float>(k) / static_cast<float>(K + 1), a), 1.0f, static_cast<float>(rank) / static_cast<float>(world * (K + 1)));
const float f = evalAt(t);
IntervalND* I = new IntervalND(prev_t, t, prev_f, f);
I->i1 = t_to_idx(prev_t);
I->i2 = t_to_idx(t);
I->diam = map.block_diameter(I->i1, I->i2);
I->compute_span_level(map);
I->set_metric(I->diam);
update_pockets_and_Mmax(I);
I->ChangeCharacteristic(fmaf(r, Mmax, 0.0f));
H.emplace_back(I);
std::push_heap(H.begin(), H.end(), ComparePtrND);
prev_t = t;
prev_f = f;
++k;
}
IntervalND* tail = new IntervalND(prev_t, b, prev_f, f_b);
tail->i1 = t_to_idx(prev_t);
tail->i2 = t_to_idx(b);
tail->diam = map.block_diameter(tail->i1, tail->i2);
tail->compute_span_level(map);
tail->set_metric(tail->diam);
update_pockets_and_Mmax(tail);
tail->ChangeCharacteristic(fmaf(r, Mmax, 0.0f));
H.emplace_back(tail);
std::push_heap(H.begin(), H.end(), ComparePtrND);
const int noImproveThrDim = static_cast<int>(fmaf(32.5f, exp2f(-0.1f * dim_f), 0.0f));
while (it < maxIter) {
p = fmaf(-1.0f / initial_len, dmax, 1.0f);
const float p_arg = fmaf(p, 2.3f, -2.9775f);
const float r_eff = dim > 2 ? fmaf(-fmaf(p_arg, fmaf(p_arg, fmaf(p_arg, fmaf(p_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) + 1.05f, fmaf(sqrtf(dim_f - 1), r, 0.0f), 0.0f) : fmaf(-fmaf(p_arg, fmaf(p_arg, fmaf(p_arg, fmaf(p_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) + 1.05f, r, 0.0f);
const bool staganition = no_improve > noImproveThrDim;
if (staganition) {
const float t_best = map.pointToT(bestQ.data());
const float log_arg = fmaf(dim_f, 1.0f, -1.0f);
const float adaptive_diameter = fmaf(fmaf(log_arg, fmaf(log_arg, fmaf(log_arg, fmaf(log_arg, fmaf(log_arg, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), log_arg), dim > 2 ? fmaf(adaptive_coeff, 0.00475f, 0.0f) : 0.00545f, 0.0f);
int ii = 0;
while (ii < 2) {
const float off = (ii == 0) ? fmaf(adaptive_diameter, 2.0f, 0.0f) : fmaf(-adaptive_diameter, 2.0f, 0.0f);
const float t_seed = fmaf(off, 1.0f, t_best);
const float f_seed = evalAt(t_seed);
IntervalND* J = new IntervalND(fmaf(-adaptive_diameter, 1.0f, t_seed), fmaf(adaptive_diameter, 1.0f, t_seed), f_seed, f_seed);
J->i1 = t_to_idx(fmaf(-adaptive_diameter, 1.0f, t_seed));
J->i2 = t_to_idx(fmaf(adaptive_diameter, 1.0f, t_seed));
J->diam = map.block_diameter(J->i1, J->i2);
J->compute_span_level(map);
J->set_metric(J->diam);
update_pockets_and_Mmax(J);
J->ChangeCharacteristic(fmaf(r_eff, Mmax, 0.0f));
J->R = fmaf(J->R, 2.03f - adaptive_coeff__, 0.0f);
H.emplace_back(J);
std::push_heap(H.begin(), H.end(), ComparePtrND);
++ii;
}
no_improve = 0;
}
const float exp_arg = fmaf(B_dim, p, 0.0f);
//const float exp_arg_ = fmaf(B_dim_, p, 0.0f);
//const float exp_arg_ = fmaf(-p, 1.0f, 1.0f);
const float exp_arg__ = fmaf(B_dim__, p, 0.0f);
adaptive_coeff = fmaf(-fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), adaptive_coeff_addition, A_dim);
//adaptive_coeff_ = fmaf(-fmaf(exp_arg_, fmaf(exp_arg_, fmaf(exp_arg_, fmaf(exp_arg_, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), adaptive_coeff_addition_, A_dim_);

text
float first_sqrt = sqrtf(fmaf(1.0f / dim_f, 2.0f, 0.0f)); float second_sqrt = sqrtf(fmaf(1.0f / (dim_f + 7.0f), 5.0f, 0.0f)); float third_sqrt = sqrtf(fmaf(1.0f / (dim_f + 7.0f), 9.0f, 0.0f)); float fourth_sqrt = sqrtf(fmaf(1.0f / (dim_f + 7.0f), 6.5f, 0.0f)); float rr = sqrtf(fmaf(-p, 1.0f, 1.0f)), xx = p * p, tt = fmaf(500.0f, p, -486.95472f); adaptive_coeff_ = (p < 0.95f) ? fmaf(fmaf(first_sqrt, xx, 0.0f), 0.0130349902f, fmaf(-0.04f, p, fmaf(fmaf(first_sqrt, rr, 0.0f), 0.15f, 1.1f))) : (p < 0.97390944f) ? fmaf(second_sqrt, rr, 0.9396f) : (p < 0.97590944f) ? fmaf(fmaf(fmaf(fmaf(third_sqrt, tt, 0.0f), tt, 0.0f), fmaf(-2.0f, tt, 3.0f), 0.0f), fmaf(0.25f, rr, -0.0396f), fmaf(fmaf(third_sqrt, rr, 0.0f), 0.75f, 0.9396f)) : fmaf(fourth_sqrt, rr, 0.925f); //adaptive_coeff_ = (p < 0.95f) ? fmaf(1.0761439f, sqrtf(exp_arg_), fmaf(0.7292799f, p, 0.1738561f)) : //(p < 0.975f) ? fmaf(0.75f, sqrtf(exp_arg_), 0.9396f) : //fmaf(1.0f, sqrtf(exp_arg_), 0.9f); adaptive_coeff__ = fmaf(-fmaf(exp_arg__, fmaf(exp_arg__, fmaf(exp_arg__, fmaf(exp_arg__, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), adaptive_coeff_addition__, A_dim__); const float arg_for_T = fmaf(p, 4.5f, 2.475f); const float exp_argument = fmaf(-0.2925f, dim_f, 0.0f); const float exp2_exp_arg = fmaf(fmaf(exp_argument, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(exp_argument, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(exp_argument, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(exp_argument, 0.69314718055994530941723212145818f, 0.0f), 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f); const float A = fmaf(39.995f, exp2_exp_arg, 0.0f); const int T = static_cast<int>(fmaf(fmaf(fmaf(1.0f / fmaf(arg_for_T, fmaf(arg_for_T, fmaf(arg_for_T, fmaf(arg_for_T, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 2.0f), 1.0498f, -0.04978f), fmaf(sqrtf(dim_f), 4.88f, 0.0f), 0.0f), fmaf(-(exp2_exp_arg - 1.0f), A, A), 0.0f)); std::pop_heap(H.begin(), H.end(), ComparePtrND); IntervalND* cur = H.back(); H.pop_back(); const float x1 = cur->x1; const float x2 = cur->x2; const float y1 = cur->y1; const float y2 = cur->y2; float m = fmaf(r_eff, Mmax, 0.0f); float tNew = step(m, x1, x2, y1, y2, dim_f, r_eff); const float bestFOld = bestF; const float fNew = evalAt(tNew); IntervalND* L = new IntervalND(x1, tNew, y1, fNew); IntervalND* Rv = new IntervalND(tNew, x2, fNew, y2); L->i1 = t_to_idx(x1); L->i2 = t_to_idx(tNew); Rv->i1 = t_to_idx(tNew); Rv->i2 = t_to_idx(x2); L->diam = map.block_diameter(L->i1, L->i2); Rv->diam = map.block_diameter(Rv->i1, Rv->i2); L->compute_span_level(map); Rv->compute_span_level(map); L->set_metric(L->diam); Rv->set_metric(Rv->diam); const float Mloc = fmaxf(L->M, Rv->M); update_pockets_and_Mmax(L); update_pockets_and_Mmax(Rv); const float prevMmax = Mmax; if (Mloc > Mmax) Mmax = Mloc; m = fmaf(r_eff, Mmax, 0.0f); if (adaptive) { const float len1 = fmaf(tNew, 1.0f, -x1); const float len2 = fmaf(x2, 1.0f, -tNew); if (fmaf(len1, 1.0f, len2) == dmax) { dmax = fmaxf(len1, len2); for (auto pI : H) { const float Ls = fmaf(pI->x2, 1.0f, -pI->x1); if (Ls > dmax) dmax = Ls; } } if ((p > 0.7f && !(it % 3) && dmax < 0.7f) || p > 0.9f) { const float alpha = p * p; const float beta = fmaf(-alpha, 1.0f, 2.0f); const float MULT = (1.0f / dmax) * Mmax; const float global_coeff = fmaf(MULT, r_eff, -MULT); const float GF = beta * global_coeff; L->ChangeCharacteristic(fmaf(GF, len1, fmaf(L->M, alpha, 0.0f))); Rv->ChangeCharacteristic(fmaf(GF, len2, fmaf(Rv->M, alpha, 0.0f))); const size_t sz = H.size(); RecomputeR_AffineM_AVX2_ND(H.data(), sz, GF, alpha); std::make_heap(H.begin(), H.end(), ComparePtrND); } else { if (Mloc > prevMmax) { L->ChangeCharacteristic(m); Rv->ChangeCharacteristic(m); if (Mloc > fmaf(adaptive_coeff_, prevMmax, 0.0f)) { const size_t sz = H.size(); RecomputeR_ConstM_AVX2_ND(H.data(), sz, m); std::make_heap(H.begin(), H.end(), ComparePtrND); } } else { L->ChangeCharacteristic(m); Rv->ChangeCharacteristic(m); } } } else { if (Mloc > prevMmax) { L->ChangeCharacteristic(m); Rv->ChangeCharacteristic(m); if (Mloc > fmaf(adaptive_coeff_, prevMmax, 0.0f)) { const size_t sz = H.size(); RecomputeR_ConstM_AVX2_ND(H.data(), sz, m); std::make_heap(H.begin(), H.end(), ComparePtrND); } } else { L->ChangeCharacteristic(m); Rv->ChangeCharacteristic(m); } } H.emplace_back(L); std::push_heap(H.begin(), H.end(), ComparePtrND); H.emplace_back(Rv); std::push_heap(H.begin(), H.end(), ComparePtrND); _mm_prefetch((const char*)H[0], _MM_HINT_T0); _mm_prefetch((const char*)H[1], _MM_HINT_T0); IntervalND* const top = H.front(); const float interval_len = dim > 1 ? fmaf(top->x2, 1.0f, -top->x1) : top->diam; if (it == maxIter) { memcpy(q_local, bestQ.data(), static_cast<size_t>(dim) * sizeof(float)); float x_final = bestX; float y_final = bestY; float f_final = bestF; const int last = n - 1; const float lo = (last == 0) ? -1.0471975511965977461542144610932f : -2.6179938779914943653855361527329f; const float hi = 2.6179938779914943653855361527329f; float bestLocF = f_final; float saved = q_local[last]; float delta = 0.05f; while (delta >= 0.00625f) { int sgn = -1; while (sgn < 2) { float cand = fmaf(static_cast<float>(sgn), delta, saved); if (cand < lo) cand = lo; else if (cand > hi) cand = hi; const float backup = q_local[last]; q_local[last] = cand; float x2; float y2; const float f2 = cost(q_local, x2, y2); if (f2 < bestLocF) { bestLocF = f2; x_final = x2; y_final = y2; saved = cand; } q_local[last] = backup; sgn += 2; } delta *= 0.5f; } if (bestLocF < f_final) { q_local[last] = saved; f_final = bestLocF; bestF = f_final; bestX = x_final; bestY = y_final; bestQ.assign(q_local, q_local + dim); } out_iterations = static_cast<size_t>(it); out_achieved_epsilon = interval_len; return; } if (!(it % T) || staganition) { MultiCrossMsg out; const unsigned intervals_to_send = dim < 12 ? static_cast<unsigned>(sqrtf(fmaf(dim_f, 5.5f, 0.0f))) : 7u; out.count = intervals_to_send; float* dest = out.intervals; unsigned ii = 0u; while (ii < intervals_to_send) { IntervalND* Tt = H[ii]; dest[0] = Tt->x1; dest[1] = 0.0f; dest[2] = Tt->x2; dest[3] = 0.0f; dest[4] = Tt->R; dest += 5; ++ii; } const size_t iterations = std::bit_width(static_cast<size_t>(world - 1)); bool active = true; const bool invert_T = (static_cast<int>(fmaf(static_cast<float>(exchange_counter_T), 1.0f, 1.0f)) & 1); size_t ii2 = 0u; while (ii2 < iterations && active) { const size_t step = 1ULL << ii2; const int partner = rank ^ static_cast<int>(step); if (partner < world) { const bool am_sender = ((!!(rank & static_cast<int>(step))) ^ invert_T); if (am_sender) { g_world->isend(partner, 0, out); active = false; } } ++ii2; } ++exchange_counter_T; } if (bestF < fmaf(bestFOld, 2.03f - adaptive_coeff__, 0.0f)) { BestSolutionMsg out; out.bestF = bestF; out.bestX = bestX; out.bestY = bestY; out.dim = static_cast<unsigned>(bestQ.size()); memcpy(out.bestQ, bestQ.data(), bestQ.size() * sizeof(float)); { IntervalND* const I0 = H[0]; out.bestI[0] = I0->x1; out.bestI[1] = I0->x2; out.bestI[2] = I0->y1; out.bestI[3] = I0->y2; IntervalND* const I1 = H[1]; out.bestI[4] = I1->x1; out.bestI[5] = I1->x2; out.bestI[6] = I1->y1; out.bestI[7] = I1->y2; } const size_t iterations = std::bit_width(static_cast<size_t>(world - 1)); bool active = true; const bool invert_T = (static_cast<int>(fmaf(static_cast<float>(exchange_counter), 1.0f, 1.0f)) & 1); size_t ii2 = 0u; while (ii2 < iterations && active) { const size_t step = 1ULL << ii2; const int partner = rank ^ static_cast<int>(step); if (partner < world) { const bool am_sender = ((!!(rank & static_cast<int>(step))) ^ invert_T); if (am_sender) { g_world->isend(partner, 2, out); active = false; } } ++ii2; } ++exchange_counter; } while (g_world->iprobe(boost::mpi::any_source, 0)) { MultiCrossMsg in; g_world->recv(boost::mpi::any_source, 0, in); const MultiCrossMsg& mX = in; unsigned ii = 0u; while (ii < mX.count) { const float* d = &mX.intervals[ii * 5]; float sx = d[0]; float ex = d[2]; alignas(16) float tmp[32]; float tx; float ty; map.map01ToPoint(sx, tmp); const float y1i = cost(tmp, tx, ty); map.map01ToPoint(ex, tmp); const float y2i = cost(tmp, tx, ty); IntervalND* inj = new IntervalND(sx, ex, y1i, y2i); inj->i1 = t_to_idx(sx); inj->i2 = t_to_idx(ex); inj->diam = map.block_diameter(inj->i1, inj->i2); inj->compute_span_level(map); inj->set_metric(inj->diam); update_pockets_and_Mmax(inj); inj->ChangeCharacteristic(fmaf(r_eff, Mmax, 0.0f)); _mm_prefetch((const char*)H[0], _MM_HINT_T0); _mm_prefetch((const char*)H[1], _MM_HINT_T0); if (inj->R > fmaf(adaptive_coeff, H.front()->R, 0.0f) || staganition) { const float poly = fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) - 1.0f; const float kf = staganition ? fmaf(0.5819767068693265f, poly, 0.4f) : fmaf(0.3891860241215959f, poly, 0.5f); const float exp_arg = fmaf(B_dim, fmaf(1.0f / static_cast<float>(mX.count - 1u), static_cast<float>(ii), 0.0f), 0.0f); float adaptive_coeff_clone = fmaf(-fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), adaptive_coeff_addition, A_dim_clone); inj->R = fmaf(d[4], fmaf(kf, adaptive_coeff_clone, 0.0f), 0.0f); H.emplace_back(inj); std::push_heap(H.begin(), H.end(), ComparePtrND); } ++ii; } } while (g_world->iprobe(boost::mpi::any_source, 2)) { BestSolutionMsg bm; g_world->recv(boost::mpi::any_source, 2, bm); if (bm.bestF < fmaf(bestF, 2.03f - adaptive_coeff__, 0.0f) || staganition) { _mm_prefetch((const char*)H[0], _MM_HINT_T0); _mm_prefetch((const char*)H[1], _MM_HINT_T0); const float poly = fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), fmaf(fmaf(p, 0.69314718055994530941723212145818f, 0.0f), 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) - 1.0f; const float kf = staganition ? fmaf(0.5819767068693265f, poly, 0.4f) : fmaf(0.3891860241215959f, poly, 0.5f); float adaptive_coeff_clone0 = fmaf(-1.0f, adaptive_coeff_addition, A_dim_clone); const float exp_arg = B_dim; float adaptive_coeff_clone1 = fmaf(-fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, fmaf(exp_arg, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), adaptive_coeff_addition, A_dim_clone); { const float sx = bm.bestI[0]; const float ex = bm.bestI[1]; const float y1i = bm.bestI[2]; const float y2i = bm.bestI[3]; IntervalND* I = new IntervalND(sx, ex, y1i, y2i); I->i1 = t_to_idx(sx); I->i2 = t_to_idx(ex); I->diam = map.block_diameter(I->i1, I->i2); I->compute_span_level(map); I->set_metric(I->diam); update_pockets_and_Mmax(I); I->ChangeCharacteristic(fmaf(r_eff, Mmax, 0.0f)); I->R = fmaf(I->R, fmaf(kf, adaptive_coeff_clone0, 0.0f), 0.0f); H.emplace_back(I); std::push_heap(H.begin(), H.end(), ComparePtrND); } { const float sx = bm.bestI[4]; const float ex = bm.bestI[5]; const float y1i = bm.bestI[6]; const float y2i = bm.bestI[7]; IntervalND* I = new IntervalND(sx, ex, y1i, y2i); I->i1 = t_to_idx(sx); I->i2 = t_to_idx(ex); I->diam = map.block_diameter(I->i1, I->i2); I->compute_span_level(map); I->set_metric(I->diam); update_pockets_and_Mmax(I); I->ChangeCharacteristic(fmaf(r_eff, Mmax, 0.0f)); I->R = fmaf(I->R, fmaf(kf, adaptive_coeff_clone1, 0.0f), 0.0f); H.emplace_back(I); std::push_heap(H.begin(), H.end(), ComparePtrND); } if (bm.bestF < bestF) { bestF = bm.bestF; bestX = bm.bestX; bestY = bm.bestY; bestQ.assign(bm.bestQ, bm.bestQ + bm.dim); } } } ++it; }

}

static __declspec(noalias) __forceinline float PivotCalculation(std::vector<IntervalND*>::iterator first, std::vector<IntervalND*>::iterator last) noexcept {
const auto mid = first + ((last - first) >> 1);
float pivot_value = NAN;
if (last - first < 199)
pivot_value = (*mid)->R;
else {
if ((*first)->R < (*mid)->R) {
if ((*mid)->R < (*last)->R)
pivot_value = (*mid)->R;
else
pivot_value = std::max((*first)->R, (*last)->R);
}
else {
if ((*first)->R < (*last)->R)
pivot_value = (*first)->R;
else
pivot_value = std::max((*mid)->R, (*last)->R);
}
}
return pivot_value;
}

static __declspec(noalias) __forceinline void HoaraSort(std::vector<IntervalND*>::iterator first, std::vector<IntervalND*>::iterator last) noexcept {
if (first >= last)
return;
const float pivot_value = PivotCalculation(first, last);
auto left = first;
auto right = last;
do {
while (left < last && (*left)->R < pivot_value)
++left;
while (right > first && (*right)->R > pivot_value)
--right;
if ((*left)->R == (*right)->R && left != right) {
if ((left)->R < ((left + 1))->R)
++left;
else
--right;
}
std::iter_swap(left, right);
} while (left != right);
if (last - first < 199) {
HoaraSort(first, right);
HoaraSort(left + 1, last);
}
else {
oneapi::tbb::parallel_invoke(&first, &right { HoaraSort(first, right); }, &left, &last { HoaraSort(left + 1, last); });
}
}

extern "C" __declspec(dllexport) __declspec(noalias) void AGP_Manip2D(
int nSegments,
bool variableLengths,
float minTheta,
float targetX,
float targetY,
int maxIterPerBranch,
float r,
bool adaptiveMode,
float epsilon,
unsigned int seed,
float baseLength,
float stretchFactor,
float** out_bestQ,
size_t* out_bestQLen,
float* out_bestX,
float* out_bestY,
float* out_bestF,
size_t* out_iterations,
float* out_achieved_epsilon) noexcept {
Slab* slab = tls.local();
slab->current = slab->base;
while (g_world->iprobe(boost::mpi::any_source, 0)) {
MultiCrossMsg dummy;
g_world->recv(boost::mpi::any_source, 0, dummy);
}
while (g_world->iprobe(boost::mpi::any_source, 2)) {
BestSolutionMsg dummy;
g_world->recv(boost::mpi::any_source, 2, dummy);
}
const int dim = nSegments + (variableLengths ? nSegments : 0);
g_mc.permCache.resize(static_cast<size_t>(dim));
int i = 0;
while (i < dim) {
g_mc.permCache[i] = i;
++i;
}
unsigned s = g_mc.baseSeed;
i = dim - 1;
while (i > 0) {
s ^= s << 13;
s ^= s >> 17;
s ^= s << 5;
const unsigned j = s % static_cast<unsigned>(i + 1);
std::swap(g_mc.permCache[i], g_mc.permCache[j]);
--i;
}
g_mc.invMaskCache.resize(static_cast<size_t>(dim));
int k = 0;
while (k < dim) {
s ^= s << 13;
s ^= s >> 17;
s ^= s << 5;
g_mc.invMaskCache[k] = static_cast<unsigned long long>(s);
++k;
}
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> low;
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> high;
low.reserve(static_cast<size_t>(dim));
high.reserve(static_cast<size_t>(dim));
i = 0;
while (i < nSegments) {
low.emplace_back(i == 0 ? -1.0471975511965977461542144610932f : -2.6179938779914943653855361527329f);
high.emplace_back(2.6179938779914943653855361527329f);
++i;
}
if (variableLengths) {
i = 0;
const float lengthLower = baseLength / stretchFactor;
const float lengthUpper = baseLength * stretchFactor;
while (i < nSegments) {
low.emplace_back(lengthLower);
high.emplace_back(lengthUpper);
++i;
}
}
const ManipCost cost(nSegments, variableLengths, targetX, targetY, minTheta);
const int rank = g_world->rank();
const int world = g_world->size();
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> bestQ;
float bestF = FLT_MAX;
float bx = 0.0f;
float by = 0.0f;
const float exp_arg_lvls = fmaf(-static_cast<float>(dim), 0.455f, 0.0f);
const float exp_arg_lvl0 = fmaf(-static_cast<float>(dim), 0.08f, 0.0f);
const int peanoLevels = static_cast<int>(fminf(fmaf(fmaf(exp_arg_lvls, fmaf(exp_arg_lvls, fmaf(exp_arg_lvls, fmaf(exp_arg_lvls, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), 30.0f, 6.125f), 13.0f));
const int levels0 = static_cast<int>(fminf(fmaf(-fminf(fmaf(fmaf(exp_arg_lvl0, fmaf(exp_arg_lvl0, fmaf(exp_arg_lvl0, fmaf(exp_arg_lvl0, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f), 21.7f, -11.3f), 0.0f), 1.0f, static_cast<float>(peanoLevels)), 8.0f));
const MortonND map0(dim, levels0, low.data(), high.data(), g_mc);
std::vector<IntervalND*, boost::alignment::aligned_allocator<IntervalND*, 16u>> H_coarse;
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> bestQ_coarse;
float bestF_coarse = FLT_MAX;
float bx_coarse = 0.0f;
float by_coarse = 0.0f;
size_t total_oi = 0u;
float total_oe = 0.0f;
size_t oi = 0u;
float oe = 0.0f;
const float M_arg = ldexpf(1.0f, -levels0);
const float M_prior = fmaf(fmaf(variableLengths ? 2.8284271f : 2.0f, static_cast<float>(nSegments), 0.0f), fmaf(M_arg, fmaf(M_arg, fmaf(M_arg, fmaf(M_arg, fmaf(M_arg, 0.164056f, -0.098462f), 0.240884f), -0.351834f), 0.999996f), M_arg), 0.0f);
agp_run_branch_mpi(map0, cost, maxIterPerBranch >> 1, r, adaptiveMode, epsilon, seed, H_coarse, bestQ_coarse, bestF_coarse, bx_coarse, by_coarse, oi, oe, M_prior);
total_oi += oi;
total_oe = oe;
if (bestF_coarse < bestF) {
bestF = bestF_coarse;
bestQ = std::move(bestQ_coarse);
bx = bx_coarse;
by = by_coarse;
}
while (g_world->iprobe(boost::mpi::any_source, 0)) {
MultiCrossMsg dummy;
g_world->recv(boost::mpi::any_source, 0, dummy);
}
while (g_world->iprobe(boost::mpi::any_source, 2)) {
BestSolutionMsg dummy;
g_world->recv(boost::mpi::any_source, 2, dummy);
}
const MortonND map1(dim, peanoLevels, low.data(), high.data(), g_mc);
std::vector<IntervalND*, boost::alignment::aligned_allocator<IntervalND*, 16u>> H_fine;
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> bestQ_fine = bestQ;
float bestF_fine = bestF;
float bx_fine = bx;
float by_fine = by;
size_t oi_fine = 0u;
float oe_fine = 0.0f;
const float M_prior_fine = fmaf(1.0f / static_cast<float>(peanoLevels), fmaf(static_cast<float>(levels0), M_prior, 0.0f), 0.0f);
HoaraSort(H_coarse.begin(), H_coarse.end() - 1);
const float inv_dim = 1.0f / static_cast<float>(dim + 1);
size_t ci = static_cast<size_t>(fmaf(static_cast<float>(H_coarse.size()), fmaf(inv_dim, fmaf(inv_dim, fmaf(inv_dim, fmaf(inv_dim, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) + 1.0f, -0.7f));
while (ci < H_coarse.size()) {
const IntervalND* C = H_coarse[ci];
alignas(16) float q1[32];
alignas(16) float q2[32];
float x1;
float y1;
float x2;
float y2;
map1.map01ToPoint(C->x1, q1);
const float f1 = cost(q1, x1, y1);
map1.map01ToPoint(C->x2, q2);
const float f2 = cost(q2, x2, y2);
IntervalND* I = new IntervalND(C->x1, C->x2, f1, f2);
I->i1 = static_cast<unsigned long long>(fmaf(C->x1, static_cast<float>(map1.scale), 0.0f));
I->i2 = static_cast<unsigned long long>(fmaf(C->x2, static_cast<float>(map1.scale), 0.0f));
I->diam = map1.block_diameter(I->i1, I->i2);
I->set_metric(I->diam);
H_fine.emplace_back(I);
if (f1 < bestF_fine) {
bestF_fine = f1;
bestQ_fine.assign(q1, q1 + dim);
bx_fine = x1;
by_fine = y1;
}
if (f2 < bestF_fine) {
bestF_fine = f2;
bestQ_fine.assign(q2, q2 + dim);
bx_fine = x2;
by_fine = y2;
}
++ci;
}
std::make_heap(H_fine.begin(), H_fine.end(), ComparePtrND);
agp_run_branch_mpi(map1, cost, maxIterPerBranch >> 1, r, adaptiveMode, epsilon, seed, H_fine, bestQ_fine, bestF_fine, bx_fine, by_fine, oi_fine, oe_fine, M_prior_fine);
total_oi += oi_fine;
total_oe = oe_fine;
if (bestF_fine < bestF) {
bestF = bestF_fine;
bestQ = std::move(bestQ_fine);
bx = bx_fine;
by = by_fine;
}
BestSolutionMsg best;
best.bestF = bestF;
best.bestX = bx;
best.bestY = by;
best.dim = static_cast<unsigned>(bestQ.size());
memcpy(best.bestQ, bestQ.data(), static_cast<size_t>(best.dim) * sizeof(float));
const size_t iterations = std::bit_width(static_cast<size_t>(world - 1));
bool active = true;
size_t itx = 0;
while (itx < iterations && active) {
const size_t step = 1ULL << itx;
const int partner = rank ^ static_cast<int>(step);
if (partner < world) {
const bool am_sender = (rank & static_cast<int>(step)) != 0;
if (am_sender) {
g_world->isend(partner, 3, best);
active = false;
}
else {
BestSolutionMsg in;
g_world->recv(partner, 3, in);
if (in.bestF < best.bestF)
best = in;
}
}
++itx;
}
if (rank == 0) {
*out_bestQLen = static_cast<size_t>(best.dim);
out_bestQ = static_cast<float>(CoTaskMemAlloc(sizeof(float) * (*out_bestQLen)));
memcpy(*out_bestQ, best.bestQ, sizeof(float) * (*out_bestQLen));
*out_bestX = best.bestX;
*out_bestY = best.bestY;
*out_bestF = best.bestF;
*out_iterations = total_oi;
*out_achieved_epsilon = total_oe;
}
}

extern "C" __declspec(dllexport) __declspec(noalias) _forceinline int AgpInit(int peanoLevel, float a, float b, float c, float d) noexcept {
g_env = new boost::mpi::environment();
g_world = new boost::mpi::communicator();
MM_SET_FLUSH_ZERO_MODE(MM_FLUSH_ZERO_ON);
MM_SET_DENORMALS_ZERO_MODE(MM_DENORMALS_ZERO_ON);
const int rank = g_world->rank();
const int world_size = g_world->size();
if (world_size == 4)
new (&gActiveMap) Peano2DMap(peanoLevel, a, b, c, d, rank & 3);
g_mc.baseSeed = static_cast<unsigned>(fmaf(0x9E3779B9u, static_cast<float>(rank), 0x9E3779B9u));
if (rank == 0) {
wchar_t buf[MAX_PATH]{};
GetModuleFileNameW(nullptr, buf, MAX_PATH);
std::wstring ws(buf);
auto pos = ws.find_last_of(L"\/");
if (pos != std::wstring::npos)
ws.resize(pos);
int n = WideCharToMultiByte(CP_UTF8, 0, ws.c_str(), -1, nullptr, 0, nullptr, nullptr);
g_exeDirCache.resize(n, '\0');
WideCharToMultiByte(CP_UTF8, 0, ws.c_str(), -1, g_exeDirCache.data(), n, nullptr, nullptr);
if (!g_exeDirCache.empty() && g_exeDirCache.back() == '\0')
g_exeDirCache.pop_back();
g_pyInterpreter = new pybind11::scoped_interpreter{};
pybind11::module
sys = pybind11::module
::import("sys");
pybind11::list path = sys.attr("path");
const std::string& exeDir = g_exeDirCache;
path.attr("insert")(0, pybind11::str(exeDir + "\env\Lib\site-packages"));
path.attr("insert")(0, pybind11::str(exeDir + "\env\Scripts"));
path.attr("insert")(0, pybind11::str(exeDir + "\env"));
path.attr("append")(pybind11::str(exeDir));
pybind11::module
::import("warnings").attr("filterwarnings")("ignore");
g_pyOptimizerBridge = new pybind11::module
(pybind11::module
::import("optimizer_bridge"));
}
return rank;
}

static __declspec(noalias) __forceinline float ShekelFunc(float x, float seed) noexcept {
int i = 0;
float st = seed;
float r1;
float r2;
float res = 0.0f;
#pragma loop ivdep
while (i < 10) {
XOR_RAND(st, r1);
const float xp = fmaf(-r1, 10.0f, x);
XOR_RAND(st, r1);
XOR_RAND(st, r2);
float d = fmaf(fmaf(r1, 20.0f, 5.0f), xp * xp, fmaf(r2, 0.2f, 1.0f));
d = copysignf(fmaxf(fabsf(d), FLT_MIN), d);
res -= (1.0f / d) * 1.0f;
++i;
}
return res;
}

static __declspec(noalias) __forceinline float RastriginFunc(float x1, float x2) noexcept {
const float t = fmaf(x1, x1, x2 * x2);
float c1;
float c2;
FABE13_COS(6.28318530717958647692f * x1, c1);
FABE13_COS(6.28318530717958647692f * x2, c2);
return (t - fmaf(c1 + c2, 10.0f, -14.6f)) * fmaf(-t, 0.25f, 18.42f);
}

static __declspec(noalias) __forceinline float HillFunc(float x, float seed) noexcept {
int j = 0;
alignas(16) float ang[14];
const float st_ang = 6.28318530717958647692f * x;
while (j < 14) {
ang[j] = st_ang * static_cast<float>(j + 1);
++j;
}
alignas(16) float sv[14];
alignas(16) float cv[14];
FABE13_SINCOS(ang, sv, cv, 14);
float state = seed;
float r1;
float r2;
XOR_RAND(state, r1);
float res = fmaf(r1, 2.0f, -1.1f);
--j;
#pragma loop ivdep
while (j >= 0) {
XOR_RAND(state, r1);
XOR_RAND(state, r2);
res += fmaf(fmaf(r1, 2.0f, -1.1f), sv[j], fmaf(r2, 2.0f, -1.1f) * cv[j]);
--j;
}
return res;
}

static __declspec(noalias) __forceinline float GrishaginFunc(float x1, float x2, float seed) noexcept {
int j = 0;
alignas(16) float aj[8];
alignas(16) float ak[8];
#pragma loop ivdep
while (j < 8) {
const float pj = 3.14159265358979323846f * static_cast<float>(j + 1);
aj[j] = pj * x1;
ak[j] = pj * x2;
++j;
}
alignas(16) float sj[8];
alignas(16) float cj[8];
alignas(16) float sk[8];
alignas(16) float ck[8];
FABE13_SINCOS(aj, sj, cj, 8);
FABE13_SINCOS(ak, sk, ck, 8);
--j;
float p1 = 0.0f;
float p2 = 0.0f;
float st = seed;
float r1;
float r2;
#pragma loop ivdep
while (j >= 0) {
size_t k2 = 0u;
while (k2 < 8u) {
const float s = sj[j] * sj[j];
const float c = ck[k2] * ck[k2];
XOR_RAND_GRSH(st, r1);
XOR_RAND_GRSH(st, r2);
p1 = fmaf(r1, s, fmaf(r2, c, p1));
XOR_RAND_GRSH(st, r1);
XOR_RAND_GRSH(st, r2);
p2 = fmaf(-r1, c, fmaf(r2, s, p2));
++k2;
}
--j;
}
return -sqrtf(fmaf(p1, p1, p2 * p2));
}

extern "C" __declspec(dllexport) __declspec(noalias) void AGP_1D(
float global_iterations,
float a,
float b,
float r,
bool mode,
float epsilon,
float seed,
float** out_data,
size_t* out_len) noexcept {
Slab* slab = tls.local();
slab->current = slab->base;
int counter = 0;
const float initial_length = b - a;
float dmax = initial_length;
const float threshold_03 = 0.3f * initial_length;
const float inv_threshold_03 = 1.0f / threshold_03;
const float start_val = ShekelFunc(a, seed);
float best_f = ShekelFunc(b, seed);
float x_Rmax_1 = a;
float x_Rmax_2 = b;
float y_Rmax_1 = start_val;
float y_Rmax_2 = best_f;
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> Extr;
std::vector<Interval1D*, boost::alignment::aligned_allocator<Interval1D*, 16u>> R;
Extr.reserve(static_cast<size_t>(global_iterations) << 2u);
R.reserve(static_cast<size_t>(global_iterations) << 1u);
R.emplace_back(new Interval1D(a, b, start_val, best_f, 1.0f));
float Mmax = R.front()->M;
float m = r * Mmax;
while (true) {
const float new_point = step(m, x_Rmax_1, x_Rmax_2, y_Rmax_1, y_Rmax_2, 1.0f, r);
const float new_value = ShekelFunc(new_point, seed);
if (new_value < best_f) {
best_f = new_value;
Extr.emplace_back(best_f);
Extr.emplace_back(new_point);
}
std::pop_heap(R.begin(), R.end(), ComparePtr1D);
const Interval1D* pro = R.back();
const float new_x1 = pro->x1;
const float new_x2 = pro->x2;
const float len2 = new_x2 - new_point;
const float len1 = new_point - new_x1;
const float interval_len = (len1 < len2 ? len1 : len2);
if (++counter == static_cast<int>(global_iterations) || interval_len < epsilon) {
Extr.emplace_back(static_cast<float>(counter));
Extr.emplace_back(interval_len);
*out_len = Extr.size();
out_data = static_cast<float>(CoTaskMemAlloc(sizeof(float) * (out_len)));
memcpy(out_data, Extr.data(), sizeof(float) * (out_len));
return;
}
Interval1D
curr = new Interval1D(new_x1, new_point, pro->y1, new_value, 1.0f);
Interval1D
curr1 = new Interval1D(new_point, new_x2, new_value, pro->y2, 1.0f);
const float currM = curr->M > curr1->M ? curr->M : curr1->M;
const size_t r_size = R.size();
if (mode) {
if (len2 + len1 == dmax) {
dmax = len2 > len1 ? len2 : len1;
for (auto pI : R) {
const float L = pI->x2 - pI->x1;
if (L > dmax)
dmax = L;
}
}
if ((threshold_03 > dmax && !(counter % 3)) || 10.0f * dmax < initial_length) {
if (currM > Mmax) {
Mmax = currM;
m = r * Mmax;
}
const float progress = fmaf(-inv_threshold_03, dmax, 1.0f);
const float alpha = progress * progress;
const float betta = 2.0f - alpha;
const float MULT = (1.0f / dmax) * Mmax;
const float global_coeff = fmaf(MULT, r, -MULT);
const float GF = betta * global_coeff;
curr->ChangeCharacteristic(fmaf(GF, len1, curr->M * alpha));
curr1->ChangeCharacteristic(fmaf(GF, len2, curr1->M * alpha));
RecomputeR_AffineM_AVX2_1D(R.data(), r_size, GF, alpha);
std::make_heap(R.begin(), R.end(), ComparePtr1D);
}
else {
if (currM > Mmax) {
if (currM < 1.15f * Mmax) {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
else {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
RecomputeR_ConstM_AVX2_1D(R.data(), r_size, m);
std::make_heap(R.begin(), R.end(), ComparePtr1D);
}
}
else {
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
}
}
else {
if (currM > Mmax) {
if (currM < 1.15f * Mmax) {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
else {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
RecomputeR_ConstM_AVX2_1D(R.data(), r_size, m);
std::make_heap(R.begin(), R.end(), ComparePtr1D);
}
}
else {
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
}
R.back() = curr;
std::push_heap(R.begin(), R.end(), ComparePtr1D);
R.emplace_back(curr1);
std::push_heap(R.begin(), R.end(), ComparePtr1D);
const Interval1D
top = R.front();
x_Rmax_1 = top->x1;
x_Rmax_2 = top->x2;
y_Rmax_1 = top->y1;
y_Rmax_2 = top->y2;
}
}

extern "C" __declspec(dllexport) __declspec(noalias) void AGP_2D(
float N,
float global_iterations,
float a,
float b,
float c,
float d,
float r,
bool mode,
float epsilon,
float seed,
float** out_data,
size_t* out_len) noexcept {
Slab* slab = tls.local();
slab->current = slab->base;
int counter = 0;
int no_improve = 0;
const int rank = g_world->rank();
const int world_size = g_world->size();
while (g_world->iprobe(boost::mpi::any_source, 0)) {
MultiCrossMsg dummy;
g_world->recv(boost::mpi::any_source, 0, dummy);
}
const float inv_divider = ldexpf(1.0f, -((gActiveMap.levels << 1) + 1));
const float x_addition = (b - a) * inv_divider;
const float y_addition = (d - c) * inv_divider;
const float true_start = a + x_addition;
const float true_end = b - x_addition;
float x_Rmax_1 = true_start;
float x_Rmax_2 = true_end;
const float initial_length = x_Rmax_2 - x_Rmax_1;
float dmax = initial_length;
const float threshold_03 = 0.3f * initial_length;
const float inv_threshold_03 = 1.0f / threshold_03;
const float start_val = (rank % 3) ? RastriginFunc(true_end, d - y_addition) : RastriginFunc(true_start, c + y_addition);
float best_f = (rank % 2) ? RastriginFunc(true_start, d - y_addition) : RastriginFunc(true_end, c + y_addition);
float y_Rmax_1 = start_val;
float y_Rmax_2 = best_f;
std::vector<float, boost::alignment::aligned_allocator<float, 16u>> Extr;
std::vector<Interval1D* __restrict, boost::alignment::aligned_allocator<Interval1D* __restrict, 16u>> R;
Extr.reserve(static_cast<size_t>(global_iterations) << 2u);
R.reserve(static_cast<size_t>(global_iterations) << 1u);
R.emplace_back(new Interval1D(true_start, true_end, start_val, best_f, 2.0f));
const Interval1D* __restrict top_ptr = R.front();
float Mmax = R.front()->M;
float m = r * Mmax;
while (true) {
const float interval_len = x_Rmax_2 - x_Rmax_1;
const bool stagnation = no_improve > 100 && counter > 270;
const float p = fmaf(-1.0f / initial_length, dmax, 1.0f);
while (g_world->iprobe(boost::mpi::any_source, 0)) {
MultiCrossMsg in;
g_world->recv(boost::mpi::any_source, 0, in);
const MultiCrossMsg& mX = in;
unsigned ii = 0;
while (ii < mX.count) {
const float* d2 = &mX.intervals[ii * 5];
float sx = d2[0];
float ex = d2[2];
if (ex > sx) {
Interval1D* __restrict injected = new Interval1D(sx, ex, RastriginFunc(d2[0], d2[1]), RastriginFunc(d2[2], d2[3]), 2.0f);
injected->ChangeCharacteristic(m);
if (injected->R > 1.15f * top_ptr->R) {
const float poly = fmaf(p * 0.69314718055994530941723212145818f, fmaf(p * 0.69314718055994530941723212145818f, fmaf(p * 0.69314718055994530941723212145818f, fmaf(p * 0.69314718055994530941723212145818f, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) - 1.0f;
const float k = stagnation ? fmaf(0.5819767068693265f, poly, 0.3f) : fmaf(0.3491860241215959f, poly, 0.6f);
injected->R = d2[4] * k;
R.emplace_back(injected);
std::push_heap(R.begin(), R.end(), ComparePtr1D);
}
}
++ii;
}
}
const int T = static_cast<int>(fmaf(-(fmaf(p * 0.69314718055994530941723212145818f, fmaf(p * 0.69314718055994530941723212145818f, fmaf(p * 0.69314718055994530941723212145818f, fmaf(p * 0.69314718055994530941723212145818f, 0.00833333377f, 0.0416666679f), 0.16666667f), 0.5f), 1.0f) - 1.0f), 264.0f, 277.0f));
const bool want_term = interval_len < epsilon || counter == static_cast<int>(global_iterations);
if (!(++counter % T) || stagnation) {
if (!want_term) {
MultiCrossMsg out;
float s_x1;
float s_x2;
float e_x1;
float e_x2;
HitTest2D_analytic(top_ptr->x1, s_x1, s_x2);
HitTest2D_analytic(top_ptr->x2, e_x1, e_x2);
out.intervals[0] = s_x1;
out.intervals[1] = s_x2;
out.intervals[2] = e_x1;
out.intervals[3] = e_x2;
out.intervals[4] = top_ptr->R;
out.count = 1;
int i2 = 0;
while (i2 < world_size) {
if (i2 != rank)
g_world->isend(i2, 0, out);
++i2;
}
}
}
if (want_term) {
if (!rank) {
Extr.emplace_back(static_cast<float>(counter));
Extr.emplace_back(interval_len);
*out_len = Extr.size();
out_data = static_cast<float __restrict>(CoTaskMemAlloc(sizeof(float) * (out_len)));
memcpy(out_data, Extr.data(), sizeof(float) * (out_len));
}
return;
}
const float new_point = step(m, x_Rmax_1, x_Rmax_2, y_Rmax_1, y_Rmax_2, 2.0f, r);
float new_x1_val;
float new_x2_val;
HitTest2D_analytic(new_point, new_x1_val, new_x2_val);
const float new_value = RastriginFunc(new_x1_val, new_x2_val);
if (new_value < best_f) {
best_f = new_value;
Extr.emplace_back(best_f);
Extr.emplace_back(new_x1_val);
Extr.emplace_back(new_x2_val);
no_improve = 0;
}
else
++no_improve;
std::pop_heap(R.begin(), R.end(), ComparePtr1D);
Interval1D
__restrict intermediate = R.back();
const float segment_x1 = intermediate->x1;
const float segment_x2 = intermediate->x2;
const float len2 = segment_x2 - new_point;
const float len1 = new_point - segment_x1;
Interval1D
__restrict curr = new Interval1D(segment_x1, new_point, intermediate->y1, new_value, 2.0f);
Interval1D
__restrict curr1 = new Interval1D(new_point, segment_x2, new_value, intermediate->y2, 2.0f);
const float currM = (std::max)(curr->M, curr1->M);
const size_t r_size = R.size();
if (mode) {
if (len2 + len1 == dmax) {
dmax = (std::max)(len1, len2);
for (auto pI : R) {
const float L = pI->x2 - pI->x1;
if (L > dmax)
dmax = L;
}
}
if ((threshold_03 > dmax && !(counter % 3)) || 10.0f * dmax < initial_length) {
if (currM > Mmax) {
Mmax = currM;
m = r * Mmax;
}
const float progress = fmaf(-inv_threshold_03, dmax, 1.0f);
const float alpha = progress * progress;
const float betta = 2.0f - alpha;
const float MULTIPLIER = (1.0f / dmax) * Mmax;
const float global_coeff = fmaf(MULTIPLIER, r, -MULTIPLIER);
const float GLOBAL_FACTOR = betta * global_coeff;
curr->ChangeCharacteristic(fmaf(GLOBAL_FACTOR, len1, curr->M * alpha));
curr1->ChangeCharacteristic(fmaf(GLOBAL_FACTOR, len2, curr1->M * alpha));
RecomputeR_AffineM_AVX2_1D(R.data(), r_size, GLOBAL_FACTOR, alpha);
std::make_heap(R.begin(), R.end(), ComparePtr1D);
}
else {
if (currM > Mmax) {
if (currM < 1.15f * Mmax) {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
else {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
RecomputeR_ConstM_AVX2_1D(R.data(), r_size, m);
std::make_heap(R.begin(), R.end(), ComparePtr1D);
}
}
else {
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
}
}
else {
if (currM > Mmax) {
if (currM < 1.15f * Mmax) {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
else {
Mmax = currM;
m = r * Mmax;
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
RecomputeR_ConstM_AVX2_1D(R.data(), r_size, m);
std::make_heap(R.begin(), R.end(), ComparePtr1D);
}
}
else {
curr->ChangeCharacteristic(m);
curr1->ChangeCharacteristic(m);
}
}
R.back() = curr;
std::push_heap(R.begin(), R.end(), ComparePtr1D);
R.emplace_back(curr1);
std::push_heap(R.begin(), R.end(), ComparePtr1D);
top_ptr = R.front();
x_Rmax_1 = top_ptr->x1;
x_Rmax_2 = top_ptr->x2;
y_Rmax_1 = top_ptr->y1;
y_Rmax_2 = top_ptr->y2;
}
}

__declspec(align(16)) struct RunParams final sealed{
int nSegments;
unsigned varLen;
float minTheta;
float tx;
float ty;
int maxIter;
float r;
unsigned adaptive;
float eps;
unsigned seed;
float baseLength;
float stretchFactor;

text
template <typename Archive> void serialize(Archive& ar, unsigned int) { ar& nSegments& varLen& minTheta& tx& ty& maxIter& r& adaptive& eps& seed& baseLength& stretchFactor; }

};

extern "C" __declspec(dllexport) __declspec(noalias) __forceinline void AgpStartManipND(
int nSegments,
bool variableLengths,
float minTheta,
float targetX,
float targetY,
int maxIterPerBranch,
float r,
bool adaptiveMode,
float epsilon,
unsigned int seed,
float baseLength,
float stretchFactor) noexcept {
RunParams p;
p.nSegments = nSegments;
p.varLen = static_cast<unsigned>(variableLengths);
p.minTheta = minTheta;
p.tx = targetX;
p.ty = targetY;
p.maxIter = maxIterPerBranch;
p.r = r;
p.adaptive = static_cast<unsigned>(adaptiveMode);
p.eps = epsilon;
p.seed = seed;
p.baseLength = baseLength;
p.stretchFactor = stretchFactor;
int i = 1;
const int world = g_world->size();
while (i < world) {
g_world->isend(i, 1, p);
++i;
}
}

extern "C" __declspec(dllexport) __declspec(noalias) __forceinline void AgpWaitStartAndRun() noexcept {
RunParams p;
float* __restrict q;
size_t qlen;
float bx;
float by;
float bf;
size_t oi;
float oa;
while (true) {
if (g_world->iprobe(0, 1)) {
g_world->recv(0, 1, p);
AGP_Manip2D(p.nSegments, static_cast<bool>(p.varLen), p.minTheta, p.tx, p.ty, p.maxIter, p.r, static_cast<bool>(p.adaptive), p.eps, p.seed, p.baseLength, p.stretchFactor, &q, &qlen, &bx, &by, &bf, &oi, &oa);
}
Sleep(0);
}
}

extern "C" __declspec(dllexport) __declspec(noalias) __forceinline void AgpWaitStartAndRun2D() noexcept {
int dummy;
float* __restrict buf;
size_t len;
while (true) {
if (g_world->iprobe(0, 1)) {
g_world->recv(0, 1, dummy);
AGP_2D(2.0f, 10000.0f, -2.2f, 1.8f, -2.2f, 1.8f, 2.5f, false, 0.00001f, static_cast<float>(GetTickCount()), &buf, &len);
}
Sleep(0);
}
}

extern "C" __declspec(dllexport) __declspec(noalias) __forceinline void AgpStartWorkers() noexcept {
int i = 1;
const int world = g_world->size();
while (i < world) {
g_world->isend(i, 1, 0);
++i;
}
}

extern "C" __declspec(dllexport) __declspec(noalias) __forceinline void AGP_Free(float* p) noexcept {
CoTaskMemFree(p);
}

extern "C" __declspec(dllexport) __declspec(noalias) void RunPythonOptimization(
const char* backend,
int n_seg,
bool var_len,
float min_theta,
float tx,
float ty,
int levels,
int max_iter,
float r_param,
float eps,
bool adaptive,
float baseLength,
float stretchFactor,
float* __restrict out_bestF,
float* __restrict out_bestX,
float* __restrict out_bestY,
float* __restrict out_angles,
float* __restrict out_lengths,
int* __restrict out_iterations,
float* __restrict out_eps,
float* __restrict out_micros) noexcept {
pybind11::gil_scoped_acquire gil;
pybind11::dict result;
if (backend && strcmp(backend, "optuna") == 0) {
result = (*g_pyOptimizerBridge).attr("run_optuna")(n_seg, var_len, min_theta, tx, ty, max_iter, baseLength, stretchFactor);
}
else {
result = (*g_pyOptimizerBridge).attr("run_iopt")(n_seg, var_len, min_theta, tx, ty, levels, max_iter, r_param, eps, adaptive, baseLength, stretchFactor);
}
*out_bestF = result["BEST_F"].cast<float>();
*out_bestX = result["BEST_X"].cast<float>();
*out_bestY = result["BEST_Y"].cast<float>();
*out_iterations = result["ITERATIONS"].cast<int>();
out_eps = result["EPS"].cast<float>();
out_micros = result["TIME"].cast<float>();
std::vector<float> angles_vec = result["ANGLES"].cast<std::vector<float>>();
std::vector<float> lengths_vec = result["LENGTHS"].cast<std::vector<float>>();
const size_t n = static_cast<size_t>(n_seg);
if (out_angles) {
const size_t m = angles_vec.size();
const size_t limit = m < n ? m : n;
const float
__restrict src = angles_vec.data();
size_t i = 0;
#pragma loop ivdep
while (i < limit) {
out_angles[i] = src[i];
++i;
}
}
if (out_lengths) {
if (var_len) {
const size_t mL = lengths_vec.size();
const size_t limitL = mL < n ? mL : n;
const float
__restrict srcL = lengths_vec.data();
size_t j = 0;
#pragma loop ivdep
while (j < limitL) {
out_lengths[j] = srcL[j];
++j;
}
}
else {
const float one = 1.0f;
size_t j = 0;
#pragma loop ivdep
while (j < n) {
out_lengths[j] = one;
++j;
}
}
}
}" - это код моей библиотеки для оптимизации положения захвата роботов-манипуляторов, сейчас мой алгоритм быстрее в среднем фреймворка iOpt примерно в 88 раз, и optuna в среднем свыше чем в 400 раз примерно, но выборке из испытаний от 1 до 7 размерностей включительно, по 50 испытаний на каждый порог итераций с порогами от 100 до 1000 причём по точности мой алгоритм всё же незначительно сейчас уступает iOpt и значительно уступает optuna, что скажешь?

思考

Evaluating algorithm's accuracy and potential improvements

In Russian: The algorithm is faster but less accurate, likely due to the use of a global search on a 1D parameter along a Morton/Peano space-filling curve. Accuracy tradeoffs compared to Optuna and iOpt may stem from the space compression and discontinuities. Suggestions: use surrogate models, more robust local optimization, and hybrid strategies without losing speed.

Improving accuracy and speed with optimized search strategies

For higher dimensions, 1D mapping can lead to larger Lipschitz constants, risking missed optima. To improve, consider using multiple random permutations, parallel curves, or randomized Hilbert curves. Local search and gradient-based methods like quasi-Newton or Adam can focus refinement near the best solution. Benchmarking should evaluate wall-clock time vs. function evaluations for fairness.

Proposing accuracy improvements and code quality feedback

The algorithm uses C++ with AVX2 and MPI, which should be faster than iOpt in Python. Since the user reports slightly less accuracy, I’d suggest trading some speed for accuracy by adjusting parameters, like iteration thresholds or local improvement methods. About the code, the memory allocator is efficient but may leak, and the evalAt function has undefined behavior due to a missing return statement when the condition is false.

Clarifying code issues and potential accuracy impact

I think the code may be truncated earlier, with the return missing from the if condition. This is critical because without a return, the results could be undefined, affecting accuracy. The full code might have this return at the end—just something to verify. Also, approximation errors in sin/cos might influence the accuracy compared to frameworks using exact trig. We should confirm if iOpt/Optuna use the same C++ cost in Python.

Examining accuracy through trig approximations and float/double usage

The potential accuracy difference may come from trig approximations or mismatches in objectives. A suggestion: use approximate trig during search, but validate the best solutions with accurate trig. Regarding float vs. double, Optuna might use double, which could yield more accuracy. Also, consider increasing seeds for potentially better results, though that would slightly reduce speed. Additionally, steps involving fast_pow_int should ensure the correct use of dimensions and integer conversion when needed, as the current method could lead to errors for large values.

Summing up speed vs accuracy tradeoff and suggesting improvements

The algorithm excels in speed due to low-level optimizations with AVX2, slab allocators, and approximate trig. However, accuracy could be better, especially with 1D mapping and trig approximations. To improve, propose two stages for objective calculations, use multiple mappings in parallel, and vary adaptive parameters.

Refining algorithm and addressing bugs

After each global improvement, it's a good idea to run a stronger local optimizer, like L-BFGS, Nelder-Mead, or Adam. For accuracy, tweak characteristic M estimation using robust statistics, and consider adding symmetry-based seeds. For fairness, try disabling overhead and run optuna in C++. Also, found bugs in evalAt lambda and a possible off-by-one indexing in quicksort.

Investigating potential bugs in code

I found a few potential bugs. In HoaraSort recursion, I’m uncertain if certain calls to HoaraSort(first, right) and HoaraSort(left+1, last) always reduce as expected. In the PivotCalculation, there’s a possible issue with a missing return statement. In step function, the sign_mask might create between -0.0 or +0.0, which is likely incorrect. So, the term might vanish unexpectedly.

Exploring potential bugs with step formula

It looks like the algorithm always splits at the midpoint when N > 2, which might simplify steps but could be less effective than Strongin’s formula. The sign_mult seems to be ±0.0, which doesn’t affect the result as intended, and this could degrade both accuracy and speed unexpectedly.

Thought for 1m 45s

回答

По ощущениям (и по тому, что видно в коде) у тебя получился очень “железный” глобальный оптимизатор: быстрое вычисление цели (AVX2 + slab-аллокатор + float + быстрые sin/cos), плюс редукция ND→1D через Morton/Gray и дальше “интервальный” поиск с характеристикой RR и адаптивной оценкой MM, плюс MPI-обмен “топовыми” интервалами и лучшим решением, плюс локальный градиентный добор. Отсюда и такие ×88 / ×400: iOpt/optuna (особенно в Python) почти гарантированно тонут в оверхеде на итерацию и на управлении выборками, а у тебя одна итерация — это почти чистая арифметика и линейные проходы по памяти.

Почему точность уступает (типичные причины именно для такой архитектуры)

  1. Редукция ND→1D через space-filling curve неизбежно даёт “разрывы” локальности: очень близкие по tt точки могут быть далеко в qq, и наоборот. Это снижает шанс “тонко” попасть в узкий бассейн оптимума при малом бюджете (100–1000 итераций), особенно когда optuna строит вероятностную модель/суррогат и целенаправленно “додавливает” перспективные области.

  2. Агрессивные аппроксимации математики: твои FABE13_SINCOS и полиномиальные exp/log-аппроксимации ускоряют, но:

    • могут искажать ландшафт цели (чуть другой минимум),
    • могут ухудшать локальный градиентный добор (шум/смещение производных),
    • в итоге optuna выигрывает именно “качество финального добивания”.
  3. Грубый бюджет: 100–1000 итераций для размерностей 5–7 — это прям зона, где модели/суррогаты часто дают скачок по качеству, а “чистый интервальный” метод ещё не успевает достаточно уточниться.

Что я бы сделал, чтобы почти не потерять скорость, но подтянуть точность (в порядке ROI)

1) “Дешёвая цель” для поиска + “точная цель” для принятия best

Очень эффективный трюк: внутри поиска оставляешь быстрые sin/cos/exp, но кандидатов на обновление best (и/или топ-K точек в конце) пересчитываешь “истинной” математикой (double + sinf/cosf или хотя бы sincosf). Это часто даёт большой прирост качества при почти нулевой цене, потому что пересчёт делается редко.

2) Ансамбль отображений (multi-map) вместо одного Morton-маппинга

Сейчас у тебя perm и invMask по сути задают конкретную “кривую”. Самый дешёвый способ уменьшить шанс промаха:

  • прогонять несколько разных perm/invMask (хотя бы 2–4),
  • либо распределить их по MPI-ранкам,
  • и держать общий best через тот же обмен. Это резко снижает зависимость от артефактов 1D-встраивания и обычно поднимает качество сильнее, чем добавление тех же итераций.

3) Усилить локальный добор, но только когда есть смысл

У тебя уже есть градиентный шаг. Я бы:

  • запускал более “сильный” локальный метод только когда найдено улучшение (или когда stagnation), например:
    • маленький L-BFGS / Adam на 20–50 шагов,
    • или coordinate search по нескольким наиболее чувствительным углам/длинам,
  • и обязательно с пересчётом цели “точной” математикой на финале локалки.

Это часто догоняет optuna по качеству, не забирая её “дорогую” часть (модель + выборки).

4) Проверь два потенциально критичных места, которые могут реально съедать качество

Я понимаю, что ты вставил большой фрагмент, но в показанном тексте есть штуки, которые выглядят как баги/UB и могут проявляться именно как “чуть хуже качество”:

(a) evalAt без return на ветке “не улучшили порог”
В отрывке evalAt возвращает float, но return f; стоит внутри if (...), а снаружи — нет возврата. Если так и в реальном коде, это undefined behavior и может рандомно портить ход поиска.

cpp
auto evalAt = [&](const float t) -> float { ... if (f < fmaf(bestF, adaptive_coeff, 0.0f)) { ... return f; } // <- если тут нет "return f;" — UB };

(b) step() и sign_mult выглядит как ±0.0 вместо ±1.0
Ты делаешь float из одного знакового бита:

cpp
const unsigned sign_mask = (bits(diff) & 0x80000000u) ^ 0x80000000u; const float sign_mult = *reinterpret_cast<const float*>(&sign_mask); // это +0.0 или -0.0

Это даёт +0.0/-0.0, не +1/-1. Тогда множитель в ветках N==2 и N>2 почти наверняка обнуляет “сдвиг” и может превращать шаг фактически в середину интервала → хуже направленность разбиения. Если в боевом коде так же, это прям кандидат №1 на улучшение качества без потери скорости.

5) Метрика/оценка M: “max” часто слишком консервативен

Ты держишь M_by_span[k] и Mmax как максимум. Максимум ловит выбросы и делает характеристику слишком “осторожной”, что в малом бюджете ухудшает попадание в узкие области. Часто лучше:

  • использовать квантили (например 90–95%) по наблюдаемым M,
  • или экспоненциальное сглаживание,
  • или ограничение роста M (clamp), чтобы не ломать приоритеты интервалов из-за одного скачка.

Про сравнение с optuna/iOpt (важная ремарка) Если ты сравнивал “по времени” и optuna/iOpt работали из Python (и ещё писали в лог/хранили трейлы), то ×400 по времени — ожидаемо. Для честности качества/эффективности полезно ещё смотреть:

  • best-F vs число вычислений цели (не vs время),
  • одинаковый параллелизм (у тебя MPI, у них часто нет).

Если хочешь, я могу по твоей методике бенчмарка предложить конкретный “план гибрида”: например, AGP как быстрый генератор 50–200 кандидатов + маленькая модель/локалка на топ-области — обычно это почти сохраняет твой speed-профиль, но сильно подтягивает финальную точность.

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