solveNP

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Nearest Neighbor for construction and 2-Opt Local Search for optimization.

Similar to Greedy, but colors the high degree nodes first.

Slower than DSatur, but better for large sparse graphs.

Try to color each node with already used colors, checking for conflicts with neighbors.

Similar to Greedy, but colors the high degree nodes first.

Slower than DSatur, but better for large sparse graphs.

Try to color each node with already used colors, checking for conflicts with neighbors.

Similar to Greedy, but colors the high degree nodes first.

Slower than DSatur, but better for large sparse graphs.

Try to color each node with already used colors, checking for conflicts with neighbors.

Slower than DSatur, but better for large sparse graphs.

Similar to Greedy, but colors the high degree nodes first.

Try to color each node with already used colors, checking for conflicts with neighbors.

Slower than DSatur, but better for large sparse graphs.

Similar to Greedy, but colors the high degree nodes first.

Try to color each node with already used colors, checking for conflicts with neighbors.

Slower than DSatur, but better for large sparse graphs.

Similar to Greedy, but colors the high degree nodes first.

Try to color each node with already used colors, checking for conflicts with neighbors.

Slower than DSatur, but better for large sparse graphs.

Similar to Greedy, but colors the high degree nodes first.

Try to color each node with already used colors, checking for conflicts with neighbors.

Slower than DSatur, but better for large sparse graphs.

Similar to Greedy, but colors the high degree nodes first.

Try to color each node with already used colors, checking for conflicts with neighbors.

Slower than DSatur, but better for large sparse graphs.

Similar to Greedy, but colors the high degree nodes first.

Try to color each node with already used colors, checking for conflicts with neighbors.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Split the graph in exactly half, take the first N/2 vertices.

Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

Pick the first N items that don't overflow the knapsack.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

Pick the first N items that don't overflow the knapsack.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

Pick the first N items that don't overflow the knapsack.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

Pick the first N items that don't overflow the knapsack.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

Pick the first N items that don't overflow the knapsack.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

Pick the first N items that don't overflow the knapsack.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

Pick the first N items that don't overflow the knapsack.

It builds a strong initial solution with value/weight greedy, then performs an exact dynamic programming optimization on a small "core" set (the least-attractive chosen items + the most-attractive unchosen items). This captures most of the optimality gap while keeping runtime practical even for 100k items.

High-performance 0-1 Knapsack solver that adaptively selects between exact dynamic programming and branch-and-bound with greedy initialization and local search improvement. Guarantees optimal solutions for DP-feasible instances; produces near-optimal solutions for larger ones.

This solution combines a greedy heuristic with an exact Branch and Bound search, optimized for speed on large datasets.

Pick the first N items that don't overflow the knapsack.