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feat: HiGHS wired as default QUBO solver in braid_search.py
- solve_qubo_highs() tried first, SA fallback on failure - build_qubo_matrix(): bracket_cost (diagonal) + crossing_penalty (off-diagonal) - find_optimal_crossing() returns method: 'highs_mip' or 'simulated_annealing' - Timing: HiGHS 8.4ms vs SA 53.5ms (6.4x speedup)
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Brandon Schneider
parent
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commit
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1 changed files with 163 additions and 18 deletions
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@ -26,6 +26,13 @@ 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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@ -224,7 +231,60 @@ def soliton_search(target_energy: float, candidates: List[dict],
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}
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# ── QUBO Optimization ───────────────────────────────────────────────────────
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# ── QUBO Matrix (dict format for HiGHS) ─────────────────────────────────────
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def bracket_cost(bracket: dict) -> float:
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"""Compute the individual (diagonal) cost for a single bracket.
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Negative cost → prefer selecting this bracket (admissible is good).
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"""
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base = 1.0 if bracket.get("admissible", False) else 2.0
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# Gap magnitude bonus: larger gaps reduce cost
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gap = bracket.get("gap", 0)
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gap_f = gap / Q16_ONE if gap < 0x80000000 else (gap - 0x100000000) / Q16_ONE
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return -base + abs(gap_f) * 0.1
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def crossing_penalty(b1: dict, b2: dict) -> float:
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"""Compute the off-diagonal interaction cost between two brackets.
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Positive penalty → discourage selecting both when they conflict.
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"""
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penalty = 0.0
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if _brackets_overlap(b1, b2):
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penalty += 2.0
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# Gap similarity: reward diversity
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g1 = _q16_to_float(b1.get("gap", 0))
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g2 = _q16_to_float(b2.get("gap", 0))
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penalty -= 0.1 * abs(g1 - g2)
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return penalty
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def build_qubo_matrix(brackets: List[dict]) -> Dict[Tuple[int, int], float]:
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"""Convert brackets to a dict-format QUBO matrix Q[i,j].
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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``.
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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) → cost coefficient.
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"""
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n = len(brackets)
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Q: Dict[Tuple[int, int], float] = {}
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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[float]]:
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"""Build a QUBO matrix from bracket pair costs.
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@ -364,45 +424,93 @@ def find_optimal_crossing(brackets: List[dict],
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max_iterations: int = 1000) -> dict:
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"""Find the optimal crossing configuration from a set of brackets.
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1. Generate all valid bracket pairs.
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2. Run QUBO optimization to select the best subset.
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3. Run soliton search on the candidates.
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1. Build QUBO matrix from brackets (dict format).
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2. Try HiGHS MIP solver first (exact, fast for small instances).
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3. Fall back to simulated annealing if HiGHS unavailable or fails.
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4. Run soliton search on the candidates.
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Args:
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brackets: list of BraidBracket dicts.
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max_iterations: iteration budget for both soliton and QUBO.
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max_iterations: iteration budget for soliton search and SA fallback.
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Returns:
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{
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"qubo_result": dict,
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"soliton_result": dict,
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"optimal_pairs": List[Tuple[dict, dict]],
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"method": str, # 'highs_mip' or 'simulated_annealing'
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}
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"""
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# Generate bracket pairs (only admissible pairs are candidates)
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pairs = []
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for i in range(len(brackets)):
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for j in range(i + 1, len(brackets)):
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if brackets[i].get("admissible", False) and brackets[j].get("admissible", False):
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pairs.append((brackets[i], brackets[j]))
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# Build dict-format QUBO matrix from raw brackets
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Q = build_qubo_matrix(brackets)
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if not pairs:
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# Fall back to all pairs even if not admissible
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qubo_result = None
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method = "simulated_annealing"
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# ── Try HiGHS first ────────────────────────────────────────────────
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if _HIGHS_AVAILABLE:
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try:
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result = solve_qubo_highs(Q, time_limit=30.0)
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selection = result.get("solution", [])
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# Convert selection vector to bracket pairs
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selected_brackets = [brackets[i] for i in range(len(selection))
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if i < len(brackets) and selection[i]]
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# Reconstruct pairs from selected brackets
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selected_pairs = []
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for i in range(len(selected_brackets)):
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for j in range(i + 1, len(selected_brackets)):
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selected_pairs.append((selected_brackets[i],
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selected_brackets[j]))
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qubo_result = {
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"selection": selection,
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"selected_pairs": selected_pairs,
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"energy": result.get("objective", 0.0),
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"iterations": 1,
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"highs_status": result.get("status", "unknown"),
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}
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method = "highs_mip"
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except Exception as exc:
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# Log and fall through to SA
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print(f"[braid_search] HiGHS failed ({exc}), falling back to SA")
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# ── Simulated annealing fallback ───────────────────────────────────
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if qubo_result is None:
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# Generate bracket pairs (only admissible pairs are candidates)
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pairs = []
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for i in range(len(brackets)):
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for j in range(i + 1, len(brackets)):
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pairs.append((brackets[i], brackets[j]))
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if (brackets[i].get("admissible", False)
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and brackets[j].get("admissible", False)):
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pairs.append((brackets[i], brackets[j]))
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# QUBO optimization
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qubo_result = qubo_optimize(pairs, max_iterations=max_iterations)
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if not pairs:
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# Fall back to all pairs even if not admissible
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for i in range(len(brackets)):
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for j in range(i + 1, len(brackets)):
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pairs.append((brackets[i], brackets[j]))
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qubo_result = qubo_optimize(pairs, max_iterations=max_iterations)
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method = "simulated_annealing"
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# Build crossing candidates from QUBO-selected pairs
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selected_pairs = qubo_result.get("selected_pairs", [])
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crossing_candidates = []
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for a, b in pairs:
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for a, b in selected_pairs:
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crossing_candidates.append({
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"brackets": [a, b],
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"admissible": a.get("admissible", False) and b.get("admissible", False),
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})
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# Fallback: if HiGHS selected nothing, build from all pairs
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if not crossing_candidates:
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for i in range(len(brackets)):
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for j in range(i + 1, len(brackets)):
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crossing_candidates.append({
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"brackets": [brackets[i], brackets[j]],
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"admissible": (brackets[i].get("admissible", False)
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and brackets[j].get("admissible", False)),
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})
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soliton_result = soliton_search(
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target_energy=float("inf"),
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candidates=crossing_candidates,
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@ -412,7 +520,8 @@ def find_optimal_crossing(brackets: List[dict],
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return {
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"qubo_result": qubo_result,
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"soliton_result": soliton_result,
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"optimal_pairs": qubo_result.get("selected_pairs", []),
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"optimal_pairs": selected_pairs if selected_pairs else qubo_result.get("selected_pairs", []),
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"method": method,
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}
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@ -426,8 +535,11 @@ def main():
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print(" assign_sidon_slots(num_slots) -> List[int]")
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print(" soliton_search(target, candidates) -> dict")
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print(" qubo_optimize(bracket_pairs) -> dict")
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print(" build_qubo_matrix(brackets) -> Dict[(i,j), float]")
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print(" find_optimal_crossing(brackets) -> dict")
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print()
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print(f" HiGHS available: {_HIGHS_AVAILABLE}")
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print()
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# Demo: Sidon set
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n = 10
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@ -438,4 +550,37 @@ def main():
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if __name__ == "__main__":
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import time
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# Generate test brackets with varying properties
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test_brackets = [
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{"admissible": True, "gap": 0x00020000, "lower": 0x00000000, "upper": 0x00030000},
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{"admissible": True, "gap": 0x00040000, "lower": 0x00010000, "upper": 0x00050000},
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{"admissible": False, "gap": 0x00010000, "lower": 0x00020000, "upper": 0x00030000},
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{"admissible": True, "gap": 0x00030000, "lower": 0x00000000, "upper": 0x00030000},
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{"admissible": True, "gap": 0x00050000, "lower": 0x00040000, "upper": 0x00090000},
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{"admissible": False, "gap": 0x00028000, "lower": 0x00010000, "upper": 0x00038000},
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]
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main()
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print()
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# ── Timing: HiGHS vs SA ────────────────────────────────────────────
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# Time HiGHS (or full pipeline with HiGHS)
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t0 = time.time()
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r1 = find_optimal_crossing(test_brackets)
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t_highs = time.time() - t0
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# Time SA fallback directly
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pairs = []
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for i in range(len(test_brackets)):
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for j in range(i + 1, len(test_brackets)):
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pairs.append((test_brackets[i], test_brackets[j]))
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t0 = time.time()
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r2 = qubo_optimize(pairs)
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t_sa = time.time() - t0
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print(f" find_optimal_crossing method: {r1.get('method', 'unknown')}")
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print(f" find_optimal_crossing: {t_highs*1000:.1f}ms | SA direct: {t_sa*1000:.1f}ms")
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print(f" HiGHS available: {_HIGHS_AVAILABLE}")
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