This page shows the benchmarks results for the dataset instance “house-sparse30”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -65.0575.
Run time 1s
| method | value | bound | optimal | accuracy |
|---|---|---|---|---|
| dd-ls0 | -65.0575 | -65.0739 | yes | 100.00% |
| dd-ls3 | -48.9874 | -66.2078 | no | 80.00% |
| dd-ls4 | -49.7224 | -71.3149 | no | 83.33% |
| fgmd | inf | -inf | no | – |
| fm-bca | -65.0575 | -65.0575 | yes | 100.00% |
| fm | -65.0575 | -78.8145 | yes | 100.00% |
| fw | 0 | -inf | no | 0.00% |
| ga | -65.0575 | -inf | yes | 100.00% |
| hbp | -65.0575 | -65.4783 | yes | 100.00% |
| ipfps | -65.0575 | -inf | yes | 100.00% |
| ipfpu | -65.0575 | -inf | yes | 100.00% |
| lsm | -61.8604 | -inf | no | 93.33% |
| mp | -65.0575 | -65.0575 | yes | 100.00% |
| mp-fw | -65.0575 | -65.0575 | yes | 100.00% |
| mpm | -60.4724 | -inf | no | 90.00% |
| mp-mcf | -65.0575 | -65.0575 | yes | 100.00% |
| pm | -52.3147 | -inf | no | 80.00% |
| rrwm | -65.0575 | -inf | yes | 100.00% |
| sm | -65.0575 | -inf | yes | 100.00% |
| smac | -12.2656 | -inf | no | 0.00% |
Run time 10s
| method | value | bound | optimal | accuracy |
|---|---|---|---|---|
| dd-ls0 | -65.0575 | -65.0739 | yes | 100.00% |
| dd-ls3 | -65.0575 | -65.0602 | yes | 100.00% |
| dd-ls4 | -65.0575 | -65.0599 | yes | 100.00% |
| fgmd | -65.0575 | -inf | yes | 100.00% |
| fm-bca | -65.0575 | -65.0575 | yes | 100.00% |
| fm | -65.0575 | -78.8145 | yes | 100.00% |
| fw | 0 | -inf | no | 0.00% |
| ga | -65.0575 | -inf | yes | 100.00% |
| hbp | -65.0575 | -65.4783 | yes | 100.00% |
| ipfps | -65.0575 | -inf | yes | 100.00% |
| ipfpu | -65.0575 | -inf | yes | 100.00% |
| lsm | -61.8604 | -inf | no | 93.33% |
| mp | -65.0575 | -65.0575 | yes | 100.00% |
| mp-fw | -65.0575 | -65.0575 | yes | 100.00% |
| mpm | -60.4724 | -inf | no | 90.00% |
| mp-mcf | -65.0575 | -65.0575 | yes | 100.00% |
| pm | -52.3147 | -inf | no | 80.00% |
| rrwm | -65.0575 | -inf | yes | 100.00% |
| sm | -65.0575 | -inf | yes | 100.00% |
| smac | -12.2656 | -inf | no | 0.00% |
Run time 100s
| method | value | bound | optimal | accuracy |
|---|---|---|---|---|
| dd-ls0 | -65.0575 | -65.0739 | yes | 100.00% |
| dd-ls3 | -65.0575 | -65.0602 | yes | 100.00% |
| dd-ls4 | -65.0575 | -65.0599 | yes | 100.00% |
| fgmd | -65.0575 | -inf | yes | 100.00% |
| fm-bca | -65.0575 | -65.0575 | yes | 100.00% |
| fm | -65.0575 | -78.8145 | yes | 100.00% |
| fw | 0 | -inf | no | 0.00% |
| ga | -65.0575 | -inf | yes | 100.00% |
| hbp | -65.0575 | -65.4783 | yes | 100.00% |
| ipfps | -65.0575 | -inf | yes | 100.00% |
| ipfpu | -65.0575 | -inf | yes | 100.00% |
| lsm | -61.8604 | -inf | no | 93.33% |
| mp | -65.0575 | -65.0575 | yes | 100.00% |
| mp-fw | -65.0575 | -65.0575 | yes | 100.00% |
| mpm | -60.4724 | -inf | no | 90.00% |
| mp-mcf | -65.0575 | -65.0575 | yes | 100.00% |
| pm | -52.3147 | -inf | no | 80.00% |
| rrwm | -65.0575 | -inf | yes | 100.00% |
| sm | -65.0575 | -inf | yes | 100.00% |
| smac | -12.2656 | -inf | no | 0.00% |
Run time 300s
| method | value | bound | optimal | accuracy |
|---|---|---|---|---|
| dd-ls0 | -65.0575 | -65.0739 | yes | 100.00% |
| dd-ls3 | -65.0575 | -65.0602 | yes | 100.00% |
| dd-ls4 | -65.0575 | -65.0599 | yes | 100.00% |
| fgmd | -65.0575 | -inf | yes | 100.00% |
| fm-bca | -65.0575 | -65.0575 | yes | 100.00% |
| fm | -65.0575 | -78.8145 | yes | 100.00% |
| fw | 0 | -inf | no | 0.00% |
| ga | -65.0575 | -inf | yes | 100.00% |
| hbp | -65.0575 | -65.4783 | yes | 100.00% |
| ipfps | -65.0575 | -inf | yes | 100.00% |
| ipfpu | -65.0575 | -inf | yes | 100.00% |
| lsm | -61.8604 | -inf | no | 93.33% |
| mp | -65.0575 | -65.0575 | yes | 100.00% |
| mp-fw | -65.0575 | -65.0575 | yes | 100.00% |
| mpm | -60.4724 | -inf | no | 90.00% |
| mp-mcf | -65.0575 | -65.0575 | yes | 100.00% |
| pm | -52.3147 | -inf | no | 80.00% |
| rrwm | -65.0575 | -inf | yes | 100.00% |
| sm | -65.0575 | -inf | yes | 100.00% |
| smac | -12.2656 | -inf | no | 0.00% |
Other Results for this Dataset
Accumulated results for whole dataset: house-sparse
Results for individual instances of the dataset: