perf: sparse Laplacian + convergence check in spectral bisection#67
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sauravbhattacharya001 wants to merge 1 commit intomasterfrom
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perf: sparse Laplacian + convergence check in spectral bisection#67sauravbhattacharya001 wants to merge 1 commit intomasterfrom
sauravbhattacharya001 wants to merge 1 commit intomasterfrom
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Replace dense O(n²) Laplacian matrix with sparse adjacency-based matrix-vector multiplication in GraphPartitioner's spectral bisection. Changes: - Sparse representation using int[][] adjacency lists instead of double[][] Laplacian matrix — O(m) per mat-vec multiply vs O(n²) - No dense matrix allocation — O(n + m) memory vs O(n²) - Convergence check (||v_new - v_old|| < 1e-10) instead of fixed 200 iterations — small/well-conditioned graphs converge in ~20 iterations - Max iterations increased to 500 for safety on ill-conditioned graphs - Removed dependency on LaplacianBuilder.buildSubgraphLaplacian for partitioning (LaplacianBuilder still available for other use cases) Performance impact for n=5000 graph: - Memory: 200 MB dense → ~100 KB sparse (typical sparse graph) - Time: 5 billion ops → ~millions (sparse + early convergence) Closes #44
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Summary
Replaces the dense O(n²) Laplacian with sparse adjacency-based matrix-vector multiplication in \GraphPartitioner.spectralBisect(), and adds a convergence check to power iteration.
Changes
Performance Impact (n=5000 typical sparse graph)
Backward Compatibility
Closes #44