Use incremental cost in Tabu Search - #275
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…ighbor Each iteration previously materialized a full (n_bitstrings, n, n) neighbor tensor and recomputed x^T Q x for every candidate flip via batched_quadratic_cost, O(n_bitstrings * n^2) per iteration. Maintain Qx incrementally instead and derive every candidate's delta from it via _flip_deltas (O(n_bitstrings * n)), with a periodic exact recompute (_REFRESH_EVERY) to bound rounding drift, mirroring simulated_annealing's approach. The final result now goes through Solution.deduplicate + _update instead of a bespoke torch.unique/batched_quadratic_cost block.
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Track tabu_search costs incrementally instead of recomputing every neighbor
Each iteration previously materialized a full (n_bitstrings, n, n) neighbor tensor and recomputed x^T Q x for every candidate flip via batched_quadratic_cost, O(n_bitstrings * n^2) per iteration. Maintain Qx incrementally instead and derive every candidate's delta from it via _flip_deltas (O(n_bitstrings * n)), with a periodic exact recompute (_REFRESH_EVERY) to bound rounding drift, mirroring simulated_annealing's approach. The final result now goes through Solution.deduplicate + _update instead of a bespoke torch.unique/batched_quadratic_cost block.