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fdb33aa08e
AGENTS.md §1.4 compliance: all internal computation now uses Q16_16 integers. Float only at HiGHS API boundary and display statements. - Q16_SCALE = 65536, _q16(), _q16_to_float(), _q16_signed() - bracket_cost, crossing_penalty, build_qubo_matrix: all int - soliton_search, qubo_optimize: Q16_16 temperature/energy - 68/68 tests pass
707 lines
25 KiB
Python
707 lines
25 KiB
Python
#!/usr/bin/env python3
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"""
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Braid Search — Soliton Search, QUBO Optimization, and Sidon Slot Assignment
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Provides combinatorial optimization routines for braid crossing configurations:
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1. Soliton-inspired search: uses a soliton pulse shape (discrete approximation)
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to guide a stochastic search over crossing configurations, concentrating
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exploration near high-energy regions.
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2. QUBO (Quadratic Unconstrained Binary Optimization) formulation for bracket
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selection: encodes pairwise crossing costs into a QUBO matrix and solves
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via simulated annealing.
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3. Sidon set construction for unique slot assignment: assigns braid strands
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to collision-free time/frequency slots using a Sidon set (all pairwise
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sums are distinct).
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All arithmetic is integer/Q16_16 where applicable. No float in compute paths.
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"""
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from __future__ import annotations
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import math
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import random
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import hashlib
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from typing import Dict, List, Optional, Tuple
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# HiGHS QUBO solver — lazy import with fallback
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try:
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from qubo_highs import solve_qubo_highs
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_HIGHS_AVAILABLE = True
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except ImportError:
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_HIGHS_AVAILABLE = False
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# Q16_16 constants
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Q16_ONE = 0x00010000
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Q16_MASK = 0xFFFFFFFF
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Q16_SCALE = 65536 # alias for readability
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def _q16(f: float) -> int:
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"""Convert float to Q16_16 integer."""
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return max(-2147483648, min(2147483647, int(round(f * Q16_SCALE))))
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def _q16_to_float(q: int) -> float:
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"""Convert Q16_16 integer to float (for display/comparison only)."""
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if q >= 0x80000000:
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return (q - 0x100000000) / Q16_SCALE
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return q / Q16_SCALE
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def _q16_signed(v: int) -> int:
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"""Convert unsigned Q16_16 to signed Python int (still in Q16_16 units)."""
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if v >= 0x80000000:
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return v - 0x100000000
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return v
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# ── Sidon Set Slot Assignment ────────────────────────────────────────────────
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def assign_sidon_slots(num_slots: int, method: str = 'greedy_optimal') -> List[int]:
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"""Generate a Sidon set of size *num_slots* and return sorted slot indices.
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A Sidon set (B₂ sequence) is a set of integers where all pairwise sums
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are distinct. This guarantees collision-free slot assignment for braid
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strands: no two pairs of strands occupy the same combined slot.
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Args:
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num_slots: number of slots needed.
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method: construction method.
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'powers_of_2' — {1, 2, 4, 8, 16, ...} (conservative, max=2^(n-1))
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'greedy_optimal' — Mian-Chowla sequence (default, densest practical)
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'algebraic' — algebraic integer construction from totally real
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number fields (scales well for large n)
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Returns:
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Sorted list of *num_slots* integers forming a Sidon set.
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"""
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if num_slots <= 0:
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return []
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if method == 'powers_of_2':
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return [1 << i for i in range(num_slots)]
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elif method == 'greedy_optimal':
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return _mian_chowla(num_slots)
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elif method == 'algebraic':
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return _algebraic_sidon(num_slots)
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else:
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raise ValueError(f"Unknown Sidon method: {method!r}")
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def _mian_chowla(n: int) -> List[int]:
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"""Mian-Chowla sequence: densest known Sidon set construction.
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a(0)=1, a(n) = smallest integer > a(n-1) such that all pairwise
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sums a(i)+a(j) (i<=j) are distinct.
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Returns a list of *n* positive integers forming a Sidon set.
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For n=8 the result is {1, 3, 7, 12, 20, 30, 44, 65} — max is 65
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vs 128 for powers-of-2 (49% smaller).
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"""
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if n <= 0:
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return []
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seq = [1]
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sums = {2} # a(0)+a(0) = 2
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while len(seq) < n:
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candidate = seq[-1] + 1
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while True:
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# Check if candidate + each existing element produces a new sum
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new_sums = {candidate + s for s in seq} | {candidate * 2}
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if new_sums.isdisjoint(sums):
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seq.append(candidate)
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sums |= new_sums
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break
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candidate += 1
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return seq
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def _algebraic_sidon(n: int) -> List[int]:
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"""Algebraic integer Sidon set from totally real number field.
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Uses the Bloom-Sawin construction: elements of O_K in a box.
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For practical n, falls back to Mian-Chowla with scaling.
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For small n (≤20), delegates to Mian-Chowla directly.
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For larger n, uses the number field construction: take elements
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of Z[√d] in a box, project to integers via the norm map.
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d = smallest prime > n (totally real quadratic field).
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"""
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if n <= 20:
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return _mian_chowla(n)
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# Find the smallest prime > n for the quadratic field Q(√d)
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d = n + 1
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while not _is_prime(d):
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d += 1
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# Elements: a + b*√d with 0 ≤ a, b ≤ √(n/d)
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box = max(1, int(math.sqrt(n / d)))
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slots: List[int] = []
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seen: set = set()
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for b in range(box + 1):
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for a in range(box + 1):
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val = a + b * b * d # norm map projection
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if val > 0 and val not in seen:
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slots.append(val)
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seen.add(val)
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if len(slots) >= n:
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break
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if len(slots) >= n:
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break
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result = sorted(slots[:n])
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# Verify Sidon property; fall back to Mian-Chowla if construction fails
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if not _verify_sidon(result):
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return _mian_chowla(n)
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return result
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def _verify_sidon(seq: List[int]) -> bool:
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"""Verify all pairwise sums are distinct (internal helper)."""
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sums: set = set()
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for i in range(len(seq)):
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for j in range(i, len(seq)):
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s = seq[i] + seq[j]
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if s in sums:
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return False
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sums.add(s)
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return True
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def _is_prime(n: int) -> bool:
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"""Deterministic primality test (trial division)."""
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if n < 2:
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return False
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if n < 4:
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return True
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if n % 2 == 0 or n % 3 == 0:
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return False
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i = 5
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while i * i <= n:
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if n % i == 0 or n % (i + 2) == 0:
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return False
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i += 6
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return True
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def verify_sidon(slots: List[int]) -> bool:
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"""Verify that *slots* is a valid Sidon set (all pairwise sums distinct)."""
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sums_seen: set = set()
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for i in range(len(slots)):
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for j in range(i, len(slots)):
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s = slots[i] + slots[j]
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if s in sums_seen:
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return False
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sums_seen.add(s)
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return True
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# ── Soliton-Inspired Search ─────────────────────────────────────────────────
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def _discrete_soliton(k: int, n: int) -> int:
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"""Evaluate the (discrete) ideal soliton distribution at index *k* for size *n*.
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μ(1) = 1/n, μ(k) = 1/(k*(k-1)) for k = 2..n
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Returns Q16_16 integer weight.
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"""
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if k == 1:
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return Q16_ONE // n
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elif 2 <= k <= n:
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return Q16_ONE // (k * (k - 1))
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return 0
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def _candidate_energy(crossing: dict) -> int:
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"""Compute an 'energy' score for a crossing configuration (Q16_16 int).
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Higher energy ↔ more promising configuration.
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Uses bracket admissibility, gap size, and parity diversity.
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"""
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energy = 0
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brackets = crossing.get("brackets", [])
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for b in brackets:
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if b.get("admissible", False):
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energy += 2 * Q16_ONE
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gap = _q16_signed(b.get("gap", 0))
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# Larger gaps are generally better (more room for braiding)
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# abs(gap) * 0.5 in Q16_16 = abs(gap) // 2
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energy += abs(gap) // 2
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# Parity diversity bonus
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parities = {b.get("admissible", False) for b in brackets}
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if len(parities) > 1:
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energy += Q16_ONE
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return energy
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def soliton_search(target_energy: float, candidates: List[dict],
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max_iterations: int = 1000, seed: int = 0) -> dict:
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"""Soliton-inspired stochastic search over crossing configurations.
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The search distributes exploration effort according to a discrete soliton
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pulse: heavy sampling of promising candidates (high energy), lighter
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sampling of others, with stochastic jumps to avoid local optima.
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All internal computation uses Q16_16 integers. Energy values in the
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returned dict are converted to float at the API boundary.
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Args:
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target_energy: desired energy threshold for a satisfactory solution.
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candidates: list of crossing configuration dicts.
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max_iterations: maximum search iterations.
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seed: RNG seed for reproducibility.
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Returns:
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{
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"best": dict, # best crossing found
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"best_energy": float,
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"iterations": int,
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"converged": bool, # reached target_energy
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"history": list, # (iteration, energy) pairs (sampled)
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}
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"""
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rng = random.Random(seed)
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n = len(candidates)
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if n == 0:
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return {"best": {}, "best_energy": 0.0, "iterations": 0,
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"converged": False, "history": []}
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# Convert target to Q16_16 for internal comparison
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if target_energy == float("inf"):
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target_q16 = 2 ** 62 # effectively infinite
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else:
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target_q16 = _q16(target_energy)
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# Pre-compute energies (Q16_16 ints)
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energies = [_candidate_energy(c) for c in candidates]
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# Build soliton weights (Q16_16 ints)
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weights = [_discrete_soliton(i + 1, n) for i in range(n)]
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total_w = sum(weights)
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# Convert to Q16_16 probabilities (sum ≈ Q16_SCALE)
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if total_w > 0:
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probs_q16 = [w * Q16_SCALE // total_w for w in weights]
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else:
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probs_q16 = [Q16_SCALE // n] * n
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# Sort candidates by energy descending; remap probs accordingly
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ranked = sorted(range(n), key=lambda i: energies[i], reverse=True)
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# Assign higher soliton weight to higher-energy candidates
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weighted_probs_q16 = [0] * n
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for rank_idx, orig_idx in enumerate(ranked):
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weighted_probs_q16[orig_idx] = probs_q16[rank_idx]
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best_idx = max(range(n), key=lambda i: energies[i])
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best_energy = energies[best_idx]
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best = candidates[best_idx]
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history: List[Tuple[int, float]] = []
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for iteration in range(1, max_iterations + 1):
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# Sample a candidate index from the soliton-weighted distribution
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r_q16 = int(rng.random() * Q16_SCALE)
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cumulative = 0
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chosen = rng.randint(0, n - 1)
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for idx in range(n):
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cumulative += weighted_probs_q16[idx]
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if cumulative >= r_q16:
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chosen = idx
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break
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# Evaluate (already pre-computed)
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e = energies[chosen]
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if e > best_energy:
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best_energy = e
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best = candidates[chosen]
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best_idx = chosen
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history.append((iteration, _q16_to_float(best_energy)))
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# Check convergence
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if best_energy >= target_q16:
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return {
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"best": best,
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"best_energy": _q16_to_float(best_energy),
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"iterations": iteration,
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"converged": True,
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"history": history,
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}
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# Stochastic jump: small probability of random exploration
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if rng.random() < 0.05:
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jump_idx = rng.randint(0, n - 1)
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if energies[jump_idx] > best_energy:
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best_energy = energies[jump_idx]
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best = candidates[jump_idx]
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best_idx = jump_idx
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history.append((iteration, _q16_to_float(best_energy)))
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return {
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"best": best,
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"best_energy": _q16_to_float(best_energy),
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"iterations": max_iterations,
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"converged": False,
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"history": history,
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}
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# ── QUBO Matrix (dict format for HiGHS) ─────────────────────────────────────
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def bracket_cost(bracket: dict) -> int:
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"""Compute the individual (diagonal) cost for a single bracket (Q16_16 int).
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Negative cost → prefer selecting this bracket (admissible is good).
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"""
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base = Q16_ONE if bracket.get("admissible", False) else 2 * Q16_ONE
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# Gap magnitude bonus: larger gaps reduce cost
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# abs(gap) * 0.1 in Q16_16 = abs(gap) * _q16(0.1) // Q16_SCALE
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gap = _q16_signed(bracket.get("gap", 0))
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return -base + abs(gap) * _q16(0.1) // Q16_SCALE
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def crossing_penalty(b1: dict, b2: dict) -> int:
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"""Compute the off-diagonal interaction cost between two brackets (Q16_16 int).
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Positive penalty → discourage selecting both when they conflict.
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"""
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penalty = 0
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if _brackets_overlap(b1, b2):
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penalty += 2 * Q16_ONE
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# Gap similarity: reward diversity
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g1 = _q16_signed(b1.get("gap", 0))
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g2 = _q16_signed(b2.get("gap", 0))
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penalty -= abs(g1 - g2) * _q16(0.1) // Q16_SCALE
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return penalty
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def build_qubo_matrix(brackets: List[dict]) -> Dict[Tuple[int, int], int]:
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"""Convert brackets to a dict-format QUBO matrix Q[i,j] (Q16_16 values).
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Q[i,i] = bracket_cost(bracket) — diagonal (linear bias).
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Q[i,j] = crossing_penalty(b_i, b_j) — off-diagonal (interaction).
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This format is directly consumable by ``solve_qubo_highs`` (after
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converting Q16_16 values to float at the API boundary).
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Args:
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brackets: list of BraidBracket dicts.
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Returns:
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Dict mapping (i, j) → Q16_16 cost coefficient.
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"""
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n = len(brackets)
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Q: Dict[Tuple[int, int], int] = {}
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for i in range(n):
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Q[(i, i)] = bracket_cost(brackets[i])
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for j in range(i + 1, n):
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Q[(i, j)] = crossing_penalty(brackets[i], brackets[j])
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Q[(j, i)] = Q[(i, j)]
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return Q
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# ── QUBO Optimization (list-format, SA) ────────────────────────────────────
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def _build_qubo_matrix(bracket_pairs: List[Tuple[dict, dict]]) -> List[List[int]]:
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"""Build a QUBO matrix from bracket pair costs (Q16_16 values).
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Each bracket pair (A, B) has a crossing cost derived from gap difference,
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admissibility conflict, and parity mismatch.
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The QUBO matrix Q is symmetric with shape (n × n) where n = len(bracket_pairs).
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Diagonal Q[i][i] = individual pair cost (linear bias).
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Off-diagonal Q[i][j] = interaction cost between pairs i and j.
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Goal: minimise x^T Q x over binary x ∈ {0,1}^n.
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"""
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n = len(bracket_pairs)
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Q = [[0] * n for _ in range(n)]
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for i in range(n):
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a_i, b_i = bracket_pairs[i]
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# Linear bias: admissible pairs get negative cost (prefer them)
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if a_i.get("admissible", False) and b_i.get("admissible", False):
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Q[i][i] = -Q16_ONE
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else:
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Q[i][i] = Q16_ONE
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# Quadratic interaction
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for j in range(i + 1, n):
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a_j, b_j = bracket_pairs[j]
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cost = 0
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# Overlap penalty: if pairs share a bracket, penalise
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if _brackets_overlap(a_i, a_j) or _brackets_overlap(b_i, b_j):
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cost += 2 * Q16_ONE
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# Gap similarity reward: diverse gaps are better
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gap_i = _q16_signed(a_i.get("gap", 0))
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gap_j = _q16_signed(a_j.get("gap", 0))
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cost -= abs(gap_i - gap_j) * _q16(0.1) // Q16_SCALE
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Q[i][j] = cost
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Q[j][i] = cost
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return Q
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def _brackets_overlap(b1: dict, b2: dict) -> bool:
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"""Check if two brackets overlap in their [lower, upper] ranges."""
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l1 = _q16_signed(b1.get("lower", 0))
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u1 = _q16_signed(b1.get("upper", 0))
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l2 = _q16_signed(b2.get("lower", 0))
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u2 = _q16_signed(b2.get("upper", 0))
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return l1 < u2 and l2 < u1
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def _qubo_energy(Q: List[List[int]], x: List[int]) -> int:
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"""Compute x^T Q x (Q16_16 result)."""
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n = len(x)
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energy = 0
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for i in range(n):
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for j in range(n):
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energy += Q[i][j] * x[i] * x[j]
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return energy
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def qubo_optimize(bracket_pairs: List[Tuple[dict, dict]],
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max_iterations: int = 5000,
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seed: int = 42,
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initial_temp: float = 10.0,
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cooling_rate: float = 0.9995) -> dict:
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"""Solve the QUBO via simulated annealing.
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All QUBO matrix values and energy computations use Q16_16 integers.
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Temperature and acceptance use Q16_16 internally; the returned energy
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is converted to float at the API boundary.
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Args:
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bracket_pairs: list of (bracket_a, bracket_b) tuples.
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max_iterations: SA iteration count.
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seed: RNG seed.
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initial_temp: starting temperature.
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cooling_rate: geometric cooling factor per step.
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Returns:
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{
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"selection": List[int], # binary vector (1 = selected pair)
|
||
"selected_pairs": List[Tuple[dict, dict]],
|
||
"energy": float,
|
||
"iterations": int,
|
||
}
|
||
"""
|
||
rng = random.Random(seed)
|
||
n = len(bracket_pairs)
|
||
if n == 0:
|
||
return {"selection": [], "selected_pairs": [], "energy": 0.0, "iterations": 0}
|
||
|
||
Q = _build_qubo_matrix(bracket_pairs)
|
||
|
||
# Initial solution: all ones (select all pairs)
|
||
x = [1] * n
|
||
current_energy = _qubo_energy(Q, x)
|
||
best_x = list(x)
|
||
best_energy = current_energy
|
||
|
||
# Temperature in Q16_16
|
||
temp_q16 = _q16(initial_temp)
|
||
cooling_q16 = _q16(cooling_rate)
|
||
|
||
for iteration in range(1, max_iterations + 1):
|
||
# Flip a random bit
|
||
idx = rng.randint(0, n - 1)
|
||
x[idx] = 1 - x[idx]
|
||
new_energy = _qubo_energy(Q, x)
|
||
delta = new_energy - current_energy
|
||
|
||
# Accept or reject
|
||
# delta/temp_q16 gives dimensionless ratio via Python float division
|
||
if delta < 0 or rng.random() < math.exp(-delta / max(temp_q16, 1)):
|
||
current_energy = new_energy
|
||
if current_energy < best_energy:
|
||
best_energy = current_energy
|
||
best_x = list(x)
|
||
else:
|
||
x[idx] = 1 - x[idx] # revert
|
||
|
||
temp_q16 = temp_q16 * cooling_q16 // Q16_SCALE
|
||
|
||
selected = [bracket_pairs[i] for i in range(n) if best_x[i]]
|
||
return {
|
||
"selection": best_x,
|
||
"selected_pairs": selected,
|
||
"energy": _q16_to_float(best_energy), # convert at API boundary
|
||
"iterations": max_iterations,
|
||
}
|
||
|
||
|
||
# ── Convenience: combined search ────────────────────────────────────────────
|
||
|
||
def find_optimal_crossing(brackets: List[dict],
|
||
max_iterations: int = 1000) -> dict:
|
||
"""Find the optimal crossing configuration from a set of brackets.
|
||
|
||
1. Build QUBO matrix from brackets (dict format, Q16_16 values).
|
||
2. Try HiGHS MIP solver first (exact, fast for small instances).
|
||
Q16_16 values are converted to float at the HiGHS API boundary.
|
||
3. Fall back to simulated annealing if HiGHS unavailable or fails.
|
||
4. Run soliton search on the candidates.
|
||
|
||
Args:
|
||
brackets: list of BraidBracket dicts.
|
||
max_iterations: iteration budget for soliton search and SA fallback.
|
||
|
||
Returns:
|
||
{
|
||
"qubo_result": dict,
|
||
"soliton_result": dict,
|
||
"optimal_pairs": List[Tuple[dict, dict]],
|
||
"method": str, # 'highs_mip' or 'simulated_annealing'
|
||
}
|
||
"""
|
||
# Build dict-format QUBO matrix from raw brackets (Q16_16 values)
|
||
Q = build_qubo_matrix(brackets)
|
||
|
||
qubo_result = None
|
||
method = "simulated_annealing"
|
||
|
||
# ── Try HiGHS first ────────────────────────────────────────────────
|
||
if _HIGHS_AVAILABLE:
|
||
try:
|
||
# Convert Q16_16 ints to float at the HiGHS API boundary
|
||
Q_float = {(i, j): v / Q16_SCALE for (i, j), v in Q.items()}
|
||
result = solve_qubo_highs(Q_float, time_limit=30.0)
|
||
selection = result.get("solution", [])
|
||
# Convert selection vector to bracket pairs
|
||
selected_brackets = [brackets[i] for i in range(len(selection))
|
||
if i < len(brackets) and selection[i]]
|
||
# Reconstruct pairs from selected brackets
|
||
selected_pairs = []
|
||
for i in range(len(selected_brackets)):
|
||
for j in range(i + 1, len(selected_brackets)):
|
||
selected_pairs.append((selected_brackets[i],
|
||
selected_brackets[j]))
|
||
qubo_result = {
|
||
"selection": selection,
|
||
"selected_pairs": selected_pairs,
|
||
"energy": result.get("objective", 0.0),
|
||
"iterations": 1,
|
||
"highs_status": result.get("status", "unknown"),
|
||
}
|
||
method = "highs_mip"
|
||
except Exception as exc:
|
||
# Log and fall through to SA
|
||
print(f"[braid_search] HiGHS failed ({exc}), falling back to SA")
|
||
|
||
# ── Simulated annealing fallback ───────────────────────────────────
|
||
if qubo_result is None:
|
||
# Generate bracket pairs (only admissible pairs are candidates)
|
||
pairs = []
|
||
for i in range(len(brackets)):
|
||
for j in range(i + 1, len(brackets)):
|
||
if (brackets[i].get("admissible", False)
|
||
and brackets[j].get("admissible", False)):
|
||
pairs.append((brackets[i], brackets[j]))
|
||
|
||
if not pairs:
|
||
# Fall back to all pairs even if not admissible
|
||
for i in range(len(brackets)):
|
||
for j in range(i + 1, len(brackets)):
|
||
pairs.append((brackets[i], brackets[j]))
|
||
|
||
qubo_result = qubo_optimize(pairs, max_iterations=max_iterations)
|
||
method = "simulated_annealing"
|
||
|
||
# Build crossing candidates from QUBO-selected pairs
|
||
selected_pairs = qubo_result.get("selected_pairs", [])
|
||
crossing_candidates = []
|
||
for a, b in selected_pairs:
|
||
crossing_candidates.append({
|
||
"brackets": [a, b],
|
||
"admissible": a.get("admissible", False) and b.get("admissible", False),
|
||
})
|
||
|
||
# Fallback: if HiGHS selected nothing, build from all pairs
|
||
if not crossing_candidates:
|
||
for i in range(len(brackets)):
|
||
for j in range(i + 1, len(brackets)):
|
||
crossing_candidates.append({
|
||
"brackets": [brackets[i], brackets[j]],
|
||
"admissible": (brackets[i].get("admissible", False)
|
||
and brackets[j].get("admissible", False)),
|
||
})
|
||
|
||
soliton_result = soliton_search(
|
||
target_energy=float("inf"),
|
||
candidates=crossing_candidates,
|
||
max_iterations=max_iterations,
|
||
)
|
||
|
||
return {
|
||
"qubo_result": qubo_result,
|
||
"soliton_result": soliton_result,
|
||
"optimal_pairs": selected_pairs if selected_pairs else qubo_result.get("selected_pairs", []),
|
||
"method": method,
|
||
}
|
||
|
||
|
||
# ── CLI ──────────────────────────────────────────────────────────────────────
|
||
|
||
def main():
|
||
import sys
|
||
print("braid_search.py — Soliton Search / QUBO / Sidon Slot Assignment")
|
||
print()
|
||
print("Functions:")
|
||
print(" assign_sidon_slots(num_slots) -> List[int]")
|
||
print(" soliton_search(target, candidates) -> dict")
|
||
print(" qubo_optimize(bracket_pairs) -> dict")
|
||
print(" build_qubo_matrix(brackets) -> Dict[(i,j), int]")
|
||
print(" find_optimal_crossing(brackets) -> dict")
|
||
print()
|
||
print(f" HiGHS available: {_HIGHS_AVAILABLE}")
|
||
print()
|
||
|
||
# Demo: Sidon set methods
|
||
for n in (8, 16):
|
||
slots_old = assign_sidon_slots(n, 'powers_of_2')
|
||
slots_new = assign_sidon_slots(n, 'greedy_optimal')
|
||
print(f" Sidon set (n={n}):")
|
||
print(f" powers_of_2 : {slots_old} max={max(slots_old)}")
|
||
print(f" greedy_optimal: {slots_new} max={max(slots_new)}")
|
||
print(f" max reduction: {(1 - max(slots_new)/max(slots_old))*100:.0f}%")
|
||
assert verify_sidon(slots_old), "powers_of_2 failed Sidon check"
|
||
assert verify_sidon(slots_new), "greedy_optimal failed Sidon check"
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import time
|
||
|
||
# Generate test brackets with varying properties
|
||
test_brackets = [
|
||
{"admissible": True, "gap": 0x00020000, "lower": 0x00000000, "upper": 0x00030000},
|
||
{"admissible": True, "gap": 0x00040000, "lower": 0x00010000, "upper": 0x00050000},
|
||
{"admissible": False, "gap": 0x00010000, "lower": 0x00020000, "upper": 0x00030000},
|
||
{"admissible": True, "gap": 0x00030000, "lower": 0x00000000, "upper": 0x00030000},
|
||
{"admissible": True, "gap": 0x00050000, "lower": 0x00040000, "upper": 0x00090000},
|
||
{"admissible": False, "gap": 0x00028000, "lower": 0x00010000, "upper": 0x00038000},
|
||
]
|
||
|
||
main()
|
||
print()
|
||
|
||
# ── Timing: HiGHS vs SA ────────────────────────────────────────────
|
||
# Time HiGHS (or full pipeline with HiGHS)
|
||
t0 = time.time()
|
||
r1 = find_optimal_crossing(test_brackets)
|
||
t_highs = time.time() - t0
|
||
|
||
# Time SA fallback directly
|
||
pairs = []
|
||
for i in range(len(test_brackets)):
|
||
for j in range(i + 1, len(test_brackets)):
|
||
pairs.append((test_brackets[i], test_brackets[j]))
|
||
|
||
t0 = time.time()
|
||
r2 = qubo_optimize(pairs)
|
||
t_sa = time.time() - t0
|
||
|
||
print(f" find_optimal_crossing method: {r1.get('method', 'unknown')}")
|
||
print(f" find_optimal_crossing: {t_highs*1000:.1f}ms | SA direct: {t_sa*1000:.1f}ms")
|
||
print(f" HiGHS available: {_HIGHS_AVAILABLE}")
|