solveNP

Travelling Salesman

Overall ranking
Rank User Solution Description
1 Vanand Gasparyan Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri Random paths
Rankings per input
small_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 6961 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 8537 Random paths
small_dense.txt
Rank User Score Solution Description
1 Vanand Gasparyan 4013 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 6982 Random paths
small_full.txt
Rank User Score Solution Description
1 Vanand Gasparyan 7898 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 9341 Random paths
medium_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 49387 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 92878 Random paths
medium_dense.txt
Rank User Score Solution Description
1 Vanand Gasparyan 15441 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 79375 Random paths
medium_full.txt
Rank User Score Solution Description
1 Vanand Gasparyan 70406 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 97111 Random paths
large_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 344101 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 349928 Random paths
large_dense.txt
Rank User Score Solution Description
1 Sam Sabouri 31029 Random paths
2 Vanand Gasparyan 32129 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
large_full.txt
Rank User Score Solution Description
1 Vanand Gasparyan 635777 Nearest Neighbor Nearest Neighbor for construction and 2-Opt Local Search for optimization.
2 Sam Sabouri 730300 Random paths

Graph Coloring

Overall ranking
Rank User Solution Description
1 Vanand Gasparyan RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
Rankings per input
small_ultra_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 6 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
1 Vanand Gasparyan 6 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 8 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
small_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 12 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
1 Vanand Gasparyan 12 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 15 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
small_dense.txt
Rank User Score Solution Description
1 Vanand Gasparyan 21 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
2 Vanand Gasparyan 22 RLF heuristic Slower than DSatur, but better for large sparse graphs.
3 Vanand Gasparyan 26 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
medium_ultra_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 25 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 27 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan 32 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
medium_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 64 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 68 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan 77 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
medium_dense.txt
Rank User Score Solution Description
1 Vanand Gasparyan 135 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 145 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan 157 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
large_ultra_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 160 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 170 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan 184 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
large_sparse.txt
Rank User Score Solution Description
1 Vanand Gasparyan 457 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 478 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan 503 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.
large_dense.txt
Rank User Score Solution Description
1 Vanand Gasparyan 1035 RLF heuristic Slower than DSatur, but better for large sparse graphs.
2 Vanand Gasparyan 1064 DSatur heuristic algorithm Similar to Greedy, but colors the high degree nodes first.
3 Vanand Gasparyan 1117 Greedy coloring Try to color each node with already used colors, checking for conflicts with neighbors.

Vertex Cover

Overall ranking
Rank User Solution Description

Maximum Clique

Overall ranking
Rank User Solution Description

Maximum Cut

Overall ranking
Rank User Solution Description
1 Vanand Gasparyan Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
Rankings per input
small_sparse_normal.txt
Rank User Score Solution Description
1 Vanand Gasparyan 132910 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 71745 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
small_sparse_uniform.txt
Rank User Score Solution Description
1 Vanand Gasparyan 122680 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 68733 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
small_normal.txt
Rank User Score Solution Description
1 Vanand Gasparyan 637014 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 75347 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
small_uniform.txt
Rank User Score Solution Description
1 Vanand Gasparyan 642985 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 72070 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
medium_sparse_normal.txt
Rank User Score Solution Description
1 Vanand Gasparyan 12432245 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 1028275 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
medium_sparse_uniform.txt
Rank User Score Solution Description
1 Vanand Gasparyan 12576002 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 778091 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
medium_normal.txt
Rank User Score Solution Description
1 Vanand Gasparyan 62527768 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 252971 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
medium_uniform.txt
Rank User Score Solution Description
1 Vanand Gasparyan 62595748 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 987956 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
large_sparse_normal.txt
Rank User Score Solution Description
1 Vanand Gasparyan 1250002901 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 9497120 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
large_sparse_uniform.txt
Rank User Score Solution Description
1 Vanand Gasparyan 1247755846 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 9851213 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
large_normal.txt
Rank User Score Solution Description
1 Vanand Gasparyan 6250745394 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 7406888 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.
large_uniform.txt
Rank User Score Solution Description
1 Vanand Gasparyan 6243753475 Lazy Split the graph in exactly half, take the first N/2 vertices.
2 Vanand Gasparyan 9978720 Greedy Iterate over the edges (heaviest to lightest) and pick one of the vertices if neither are part of the vertex subset.

Steiner Tree

Overall ranking
Rank User Solution Description

Knapsack

Overall ranking
Rank User Solution Description