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01 / marketsplit

Market Split

Multi-dimensional Subset Sum
Binary QUBO / ILP combinatorial NP-hard subset-sum
Instances 156
Optimally solved 115 / 156
Variable range 20–140
Objective minimize

Overview

The Market Split Problem is a multi-dimensional variant of the classic subset sum problem, where multiple constraints must be satisfied simultaneously. Each row represents its own subset sum problem, making this a challenging combinatorial optimization task.

Problem Description

Given a matrix $A \in \mathbb{N}^{m,n}$ and a right-hand side vector $b \in \mathbb{N}^m$, find a feasible binary vector $x \in \{0,1\}^{n}$ that satisfies:

$$ Ax = b $$

where each row $i \in \{1,\ldots,m\}$ represents an independent subset sum constraint that must be fulfilled simultaneously.

Ax = b on {0,1}ⁿ

Performance

Runtime to reach best-known objective

Sorted instances vs total runtime. A point (x, y) means x instances were solved within y seconds. Solid line + filled circle = proven exact; dashed line + open diamond = heuristic. Lower-right is better.

Classical (115 · 3 exact, 112 heuristic) Quantum hardware (3 · heuristic)
Cactus plot: cumulative number of instances solved (horizontal) versus total runtime in seconds on a log scale (vertical), one line per method group; lower-right is better.1101001,00010,0001.0e+51.0e+6020406080100115instances solved →runtime (s, log)Classical · heuristic · ms_03_050_002 · 0 sClassical · heuristic · ms_03_050_005 · 0 sClassical · heuristic · ms_03_050_007 · 0 sClassical · heuristic · ms_03_050_009 · 0 sClassical · heuristic · ms_03_100_012 · 0 sClassical · heuristic · ms_03_100_019 · 0 sClassical · heuristic · ms_03_100_022 · 0 sClassical · heuristic · ms_03_200_050 · 0 sClassical · heuristic · ms_03_200_068 · 0 sClassical · heuristic · ms_03_200_161 · 0 sClassical · heuristic · ms_03_200_177 · 0 sClassical · heuristic · ms_04_050_001 · 0 sClassical · heuristic · ms_04_050_003 · 0 sClassical · heuristic · ms_04_050_004 · 0 sClassical · heuristic · ms_04_050_005 · 0 sClassical · heuristic · ms_04_100_003 · 0 sClassical · heuristic · ms_04_100_009 · 0 sClassical · heuristic · ms_04_100_013 · 0 sClassical · heuristic · ms_04_100_015 · 0 sClassical · heuristic · ms_04_200_030 · 0 sClassical · heuristic · ms_04_200_150 · 0 sClassical · heuristic · ms_04_200_174 · 0 sClassical · heuristic · ms_04_200_176 · 0 sClassical · heuristic · ms_05_050_002 · 0 sClassical · heuristic · ms_05_050_003 · 0 sClassical · heuristic · ms_05_050_004 · 0 sClassical · heuristic · ms_05_100_003 · 0 sClassical · heuristic · ms_05_100_006 · 0 sClassical · heuristic · ms_05_100_013 · 0 sClassical · heuristic · ms_05_200_070 · 0 sClassical · heuristic · ms_05_200_095 · 0 sClassical · heuristic · ms_05_200_180 · 0 sClassical · heuristic · ms_05_200_199 · 0 sClassical · heuristic · ms_06_050_001 · 0 sClassical · heuristic · ms_06_050_002 · 0 sClassical · heuristic · ms_06_050_003 · 0 sClassical · heuristic · ms_06_050_004 · 0 sClassical · heuristic · ms_06_100_003 · 0 sClassical · heuristic · ms_06_100_010 · 0 sClassical · heuristic · ms_06_200_104 · 0 sClassical · heuristic · ms_06_200_289 · 0 sClassical · heuristic · ms_07_050_001 · 0 sClassical · heuristic · ms_07_050_002 · 0 sClassical · heuristic · ms_07_050_004 · 0 sClassical · heuristic · ms_07_100_003 · 0 sClassical · heuristic · ms_07_100_006 · 0 sClassical · heuristic · ms_07_200_370 · 0 sClassical · heuristic · ms_08_050_001 · 0 sClassical · heuristic · ms_08_200_001 · 0 sClassical · heuristic · ms_09_050_000 · 0 sClassical · heuristic · ms_09_050_002 · 0 sClassical · heuristic · ms_09_100_000 · 0 sClassical · heuristic · ms_09_200_002 · 0 sClassical · exact · ms_03_100_001 · 0.23 sClassical · exact · ms_05_050_001 · 0.7 sClassical · exact · ms_05_100_015 · 0.92 sClassical · heuristic · ms_06_100_002 · 1 sClassical · heuristic · ms_06_100_005 · 2 sClassical · heuristic · ms_06_200_077 · 2 sClassical · heuristic · ms_06_200_240 · 2 sClassical · heuristic · ms_07_050_003 · 2 sClassical · heuristic · ms_07_200_500 · 2 sClassical · heuristic · ms_09_050_001 · 2 sClassical · heuristic · ms_07_100_002 · 3 sClassical · heuristic · ms_07_100_005 · 3 sClassical · heuristic · ms_08_050_003 · 3 sClassical · heuristic · ms_09_050_003 · 3 sClassical · heuristic · ms_08_100_002 · 4 sClassical · heuristic · ms_09_100_003 · 4 sClassical · heuristic · ms_07_200_248 · 5 sClassical · heuristic · ms_07_200_398 · 5 sClassical · heuristic · ms_08_050_000 · 5 sClassical · heuristic · ms_10_050_000 · 5 sClassical · heuristic · ms_08_100_003 · 6 sClassical · heuristic · ms_08_200_000 · 6 sClassical · heuristic · ms_10_050_001 · 6 sClassical · heuristic · ms_08_050_002 · 7 sClassical · heuristic · ms_08_100_001 · 7 sClassical · heuristic · ms_10_050_003 · 7 sClassical · heuristic · ms_11_050_002 · 7 sClassical · heuristic · ms_08_100_000 · 8 sClassical · heuristic · ms_08_200_002 · 9 sClassical · heuristic · ms_08_200_003 · 9 sClassical · heuristic · ms_10_050_002 · 9 sClassical · heuristic · ms_09_100_001 · 10 sClassical · heuristic · ms_09_200_003 · 15 sClassical · heuristic · ms_11_050_000 · 16 sClassical · heuristic · ms_11_050_001 · 24 sClassical · heuristic · ms_09_100_002 · 26 sClassical · heuristic · ms_09_200_001 · 37 sClassical · heuristic · ms_11_050_003 · 49 sClassical · heuristic · ms_09_200_000 · 142 sClassical · heuristic · ms_12_050_003 · 323 sClassical · heuristic · ms_12_050_002 · 376 sClassical · heuristic · ms_10_100_000 · 443 sClassical · heuristic · ms_12_050_001 · 539 sClassical · heuristic · ms_10_100_001 · 593 sClassical · heuristic · ms_10_100_002 · 1,045 sClassical · heuristic · ms_12_050_000 · 1,230 sClassical · heuristic · ms_10_200_002 · 1,491 sClassical · heuristic · ms_10_100_003 · 2,597 sClassical · heuristic · ms_11_200_003 · 5,215 sClassical · heuristic · ms_10_200_000 · 6,386 sClassical · heuristic · ms_11_100_002 · 7,640 sClassical · heuristic · ms_10_200_001 · 13,937 sClassical · heuristic · ms_11_100_000 · 14,233 sClassical · heuristic · ms_10_200_003 · 33,712 sClassical · heuristic · ms_12_100_001 · 79,188 sClassical · heuristic · ms_11_100_003 · 86,000 sClassical · heuristic · ms_12_100_000 · 90,232 sClassical · heuristic · ms_11_100_001 · 1.7e+5 sClassical · heuristic · ms_11_200_002 · 2.4e+5 sClassical · heuristic · ms_11_200_001 · 2.7e+5 sClassical · heuristic · ms_12_100_002 · 1.2e+6 sClassical · heuristic · ms_11_200_000 · 1.3e+6 sQuantum hardware · heuristic · ms_03_200_177 · 125.1 sQuantum hardware · heuristic · ms_03_100_022 · 136.6 sQuantum hardware · heuristic · ms_03_050_002 · 144.4 s

Time-to-solution (TTS) to reach best-known objective

Same as the runtime cactus but uses the reported Time-to-Solution rather than total runtime. Solid = exact, dashed = heuristic.

Classical (60 · 57 exact, 3 heuristic) Quantum hardware (3 · heuristic)
Cactus plot: cumulative number of instances solved (horizontal) versus time-to-solution in seconds on a log scale (vertical), one line per method group; lower-right is better.0.010.11101001,0000102030405060instances solved →time-to-solution (s, log)Classical · exact · ms_03_050_002 · 0 sClassical · exact · ms_03_050_005 · 0 sClassical · exact · ms_03_050_007 · 0 sClassical · exact · ms_03_050_009 · 0 sClassical · exact · ms_03_100_001 · 0 sClassical · exact · ms_03_100_012 · 0 sClassical · exact · ms_03_100_019 · 0 sClassical · exact · ms_03_100_022 · 0 sClassical · exact · ms_03_200_050 · 0 sClassical · exact · ms_03_200_068 · 0 sClassical · exact · ms_03_200_161 · 0 sClassical · exact · ms_03_200_177 · 0 sClassical · exact · ms_04_050_003 · 0 sClassical · exact · ms_04_050_005 · 0 sClassical · exact · ms_04_100_003 · 0 sClassical · exact · ms_04_100_009 · 0 sClassical · exact · ms_04_100_013 · 0 sClassical · exact · ms_04_200_030 · 0 sClassical · exact · ms_04_200_150 · 0 sClassical · exact · ms_04_200_174 · 0 sClassical · exact · ms_04_200_176 · 0 sClassical · exact · ms_05_050_001 · 0 sClassical · exact · ms_05_050_002 · 0 sClassical · exact · ms_05_050_003 · 0 sClassical · exact · ms_05_050_004 · 0 sClassical · exact · ms_05_100_003 · 0 sClassical · exact · ms_05_100_006 · 0 sClassical · exact · ms_05_100_013 · 0 sClassical · exact · ms_05_100_015 · 0 sClassical · exact · ms_05_200_070 · 0 sClassical · exact · ms_05_200_199 · 0 sClassical · heuristic · ms_04_050_001 · 0.01 sClassical · heuristic · ms_04_100_015 · 0.02 sClassical · heuristic · ms_04_050_004 · 0.2 sClassical · exact · ms_05_200_095 · 1 sClassical · exact · ms_05_200_180 · 1 sClassical · exact · ms_06_050_001 · 6 sClassical · exact · ms_06_050_003 · 11 sClassical · exact · ms_06_050_002 · 13 sClassical · exact · ms_06_200_077 · 15 sClassical · exact · ms_06_200_289 · 22 sClassical · exact · ms_06_050_004 · 26 sClassical · exact · ms_06_200_240 · 27 sClassical · exact · ms_06_200_104 · 28 sClassical · exact · ms_06_100_002 · 45 sClassical · exact · ms_06_100_003 · 61 sClassical · exact · ms_06_100_005 · 70 sClassical · exact · ms_06_100_010 · 113 sClassical · exact · ms_07_050_003 · 160 sClassical · exact · ms_07_050_002 · 166 sClassical · exact · ms_07_050_001 · 206 sClassical · exact · ms_07_050_004 · 246 sClassical · exact · ms_07_100_005 · 886 sClassical · exact · ms_07_100_006 · 898 sClassical · exact · ms_07_100_003 · 1,139 sClassical · exact · ms_07_100_002 · 1,475 sClassical · exact · ms_07_200_248 · 2,329 sClassical · exact · ms_07_200_398 · 2,479 sClassical · exact · ms_07_200_370 · 2,568 sClassical · exact · ms_07_200_500 · 2,753 sQuantum hardware · heuristic · ms_03_200_177 · 125.1 sQuantum hardware · heuristic · ms_03_100_022 · 136.6 sQuantum hardware · heuristic · ms_03_050_002 · 144.4 s

Runtime scaling with instance size

Fastest feasible runtime (log scale) per instance versus Variables — shows how each group scales.

Classical (115) Quantum hardware (3)
Scaling plot: fastest feasible runtime in seconds on a log scale (vertical) versus Variables (horizontal), one series per method group.1101001,00010,0001.0e+51.0e+620406080100Variables →runtime (s, log)Classical · ms_03_050_002 · Variables 20 · 0 sClassical · ms_03_050_005 · Variables 20 · 0 sClassical · ms_03_050_007 · Variables 20 · 0 sClassical · ms_03_050_009 · Variables 20 · 0 sClassical · ms_03_100_001 · Variables 20 · 0.23 sClassical · ms_03_100_012 · Variables 20 · 0 sClassical · ms_03_100_019 · Variables 20 · 0 sClassical · ms_03_100_022 · Variables 20 · 0 sClassical · ms_03_200_050 · Variables 20 · 0 sClassical · ms_03_200_068 · Variables 20 · 0 sClassical · ms_03_200_161 · Variables 20 · 0 sClassical · ms_03_200_177 · Variables 20 · 0 sClassical · ms_04_050_001 · Variables 30 · 0 sClassical · ms_04_050_003 · Variables 30 · 0 sClassical · ms_04_050_004 · Variables 30 · 0 sClassical · ms_04_050_005 · Variables 30 · 0 sClassical · ms_04_100_003 · Variables 30 · 0 sClassical · ms_04_100_009 · Variables 30 · 0 sClassical · ms_04_100_013 · Variables 30 · 0 sClassical · ms_04_100_015 · Variables 30 · 0 sClassical · ms_04_200_030 · Variables 30 · 0 sClassical · ms_04_200_150 · Variables 30 · 0 sClassical · ms_04_200_174 · Variables 30 · 0 sClassical · ms_04_200_176 · Variables 30 · 0 sClassical · ms_05_050_001 · Variables 40 · 0.7 sClassical · ms_05_050_002 · Variables 40 · 0 sClassical · ms_05_050_003 · Variables 40 · 0 sClassical · ms_05_050_004 · Variables 40 · 0 sClassical · ms_05_100_003 · Variables 40 · 0 sClassical · ms_05_100_006 · Variables 40 · 0 sClassical · ms_05_100_013 · Variables 40 · 0 sClassical · ms_05_100_015 · Variables 40 · 0.92 sClassical · ms_05_200_070 · Variables 40 · 0 sClassical · ms_05_200_095 · Variables 40 · 0 sClassical · ms_05_200_180 · Variables 40 · 0 sClassical · ms_05_200_199 · Variables 40 · 0 sClassical · ms_06_050_001 · Variables 50 · 0 sClassical · ms_06_050_002 · Variables 50 · 0 sClassical · ms_06_050_003 · Variables 50 · 0 sClassical · ms_06_050_004 · Variables 50 · 0 sClassical · ms_06_100_002 · Variables 50 · 1 sClassical · ms_06_100_003 · Variables 50 · 0 sClassical · ms_06_100_005 · Variables 50 · 2 sClassical · ms_06_100_010 · Variables 50 · 0 sClassical · ms_06_200_077 · Variables 50 · 2 sClassical · ms_06_200_104 · Variables 50 · 0 sClassical · ms_06_200_240 · Variables 50 · 2 sClassical · ms_06_200_289 · Variables 50 · 0 sClassical · ms_07_050_001 · Variables 60 · 0 sClassical · ms_07_050_002 · Variables 60 · 0 sClassical · ms_07_050_003 · Variables 60 · 2 sClassical · ms_07_050_004 · Variables 60 · 0 sClassical · ms_07_100_002 · Variables 60 · 3 sClassical · ms_07_100_003 · Variables 60 · 0 sClassical · ms_07_100_005 · Variables 60 · 3 sClassical · ms_07_100_006 · Variables 60 · 0 sClassical · ms_07_200_248 · Variables 60 · 5 sClassical · ms_07_200_370 · Variables 60 · 0 sClassical · ms_07_200_398 · Variables 60 · 5 sClassical · ms_07_200_500 · Variables 60 · 2 sClassical · ms_08_050_000 · Variables 70 · 5 sClassical · ms_08_050_001 · Variables 70 · 0 sClassical · ms_08_050_002 · Variables 70 · 7 sClassical · ms_08_050_003 · Variables 70 · 3 sClassical · ms_08_100_000 · Variables 70 · 8 sClassical · ms_08_100_001 · Variables 70 · 7 sClassical · ms_08_100_002 · Variables 70 · 4 sClassical · ms_08_100_003 · Variables 70 · 6 sClassical · ms_08_200_000 · Variables 70 · 6 sClassical · ms_08_200_001 · Variables 70 · 0 sClassical · ms_08_200_002 · Variables 70 · 9 sClassical · ms_08_200_003 · Variables 70 · 9 sClassical · ms_09_050_000 · Variables 80 · 0 sClassical · ms_09_050_001 · Variables 80 · 2 sClassical · ms_09_050_002 · Variables 80 · 0 sClassical · ms_09_050_003 · Variables 80 · 3 sClassical · ms_09_100_000 · Variables 80 · 0 sClassical · ms_09_100_001 · Variables 80 · 10 sClassical · ms_09_100_002 · Variables 80 · 26 sClassical · ms_09_100_003 · Variables 80 · 4 sClassical · ms_09_200_000 · Variables 80 · 142 sClassical · ms_09_200_001 · Variables 80 · 37 sClassical · ms_09_200_002 · Variables 80 · 0 sClassical · ms_09_200_003 · Variables 80 · 15 sClassical · ms_10_050_000 · Variables 90 · 5 sClassical · ms_10_050_001 · Variables 90 · 6 sClassical · ms_10_050_002 · Variables 90 · 9 sClassical · ms_10_050_003 · Variables 90 · 7 sClassical · ms_10_100_000 · Variables 90 · 443 sClassical · ms_10_100_001 · Variables 90 · 593 sClassical · ms_10_100_002 · Variables 90 · 1,045 sClassical · ms_10_100_003 · Variables 90 · 2,597 sClassical · ms_10_200_000 · Variables 90 · 6,386 sClassical · ms_10_200_001 · Variables 90 · 13,937 sClassical · ms_10_200_002 · Variables 90 · 1,491 sClassical · ms_10_200_003 · Variables 90 · 33,712 sClassical · ms_11_050_000 · Variables 100 · 16 sClassical · ms_11_050_001 · Variables 100 · 24 sClassical · ms_11_050_002 · Variables 100 · 7 sClassical · ms_11_050_003 · Variables 100 · 49 sClassical · ms_11_100_000 · Variables 100 · 14,233 sClassical · ms_11_100_001 · Variables 100 · 1.7e+5 sClassical · ms_11_100_002 · Variables 100 · 7,640 sClassical · ms_11_100_003 · Variables 100 · 86,000 sClassical · ms_11_200_000 · Variables 100 · 1.3e+6 sClassical · ms_11_200_001 · Variables 100 · 2.7e+5 sClassical · ms_11_200_002 · Variables 100 · 2.4e+5 sClassical · ms_11_200_003 · Variables 100 · 5,215 sClassical · ms_12_050_000 · Variables 110 · 1,230 sClassical · ms_12_050_001 · Variables 110 · 539 sClassical · ms_12_050_002 · Variables 110 · 376 sClassical · ms_12_050_003 · Variables 110 · 323 sClassical · ms_12_100_000 · Variables 110 · 90,232 sClassical · ms_12_100_001 · Variables 110 · 79,188 sClassical · ms_12_100_002 · Variables 110 · 1.2e+6 sQuantum hardware · ms_03_050_002 · Variables 20 · 144.4 sQuantum hardware · ms_03_100_022 · Variables 20 · 136.6 sQuantum hardware · ms_03_200_177 · Variables 20 · 125.1 s

Submissions (6)

Method Submitter Type Date Instances
Parity_Twine ParityQC Team Quantum HW 2026-07-28 10
SA Q-Bridge Team Classical 2026-07-09 4
Arvak Daniel Hinderink Quantum sim 2026-03-08 1
Lattice Alfred Wassermann Classical 2025-07-15 115
MIP Maximilian Schicker Classical 2024-12-23 60
Abs2 Maximilian Schicker Classical 2024-12-22 60

Instances (156)

156 of 156
Name Markets Variables Coeff. range Best objective Source Status Download
ms_03_050_002 3 20 50 0 Reference solution Optimal ↓ raw
ms_03_050_005 3 20 50 0 Reference solution Optimal ↓ raw
ms_03_050_007 3 20 50 0 Reference solution Optimal ↓ raw
ms_03_050_009 3 20 50 0 Reference solution Optimal ↓ raw
ms_03_100_001 3 20 100 0 Reference solution Optimal ↓ raw
ms_03_100_012 3 20 100 0 Reference solution Optimal ↓ raw
ms_03_100_019 3 20 100 0 Reference solution Optimal ↓ raw
ms_03_100_022 3 20 100 0 Reference solution Optimal ↓ raw
ms_03_200_050 3 20 200 0 Reference solution Optimal ↓ raw
ms_03_200_068 3 20 200 0 Reference solution Optimal ↓ raw
ms_03_200_161 3 20 200 0 Reference solution Optimal ↓ raw
ms_03_200_177 3 20 200 0 Reference solution Optimal ↓ raw
ms_04_050_001 4 30 50 0 Reference solution Optimal ↓ raw
ms_04_050_003 4 30 50 0 Reference solution Optimal ↓ raw
ms_04_050_004 4 30 50 0 Reference solution Optimal ↓ raw
ms_04_050_005 4 30 50 0 Reference solution Optimal ↓ raw
ms_04_100_003 4 30 100 0 Reference solution Optimal ↓ raw
ms_04_100_009 4 30 100 0 Reference solution Optimal ↓ raw
ms_04_100_013 4 30 100 0 Reference solution Optimal ↓ raw
ms_04_100_015 4 30 100 0 Reference solution Optimal ↓ raw
ms_04_200_030 4 30 200 0 Reference solution Optimal ↓ raw
ms_04_200_150 4 30 200 0 Reference solution Optimal ↓ raw
ms_04_200_174 4 30 200 0 Reference solution Optimal ↓ raw
ms_04_200_176 4 30 200 0 Reference solution Optimal ↓ raw
ms_05_050_001 5 40 50 0 Reference solution Optimal ↓ raw
ms_05_050_002 5 40 50 0 Reference solution Optimal ↓ raw
ms_05_050_003 5 40 50 0 Reference solution Optimal ↓ raw
ms_05_050_004 5 40 50 0 Reference solution Optimal ↓ raw
ms_05_100_003 5 40 100 0 Reference solution Optimal ↓ raw
ms_05_100_006 5 40 100 0 Reference solution Optimal ↓ raw
ms_05_100_013 5 40 100 0 Reference solution Optimal ↓ raw
ms_05_100_015 5 40 100 0 Reference solution Optimal ↓ raw
ms_05_200_070 5 40 200 0 Reference solution Optimal ↓ raw
ms_05_200_095 5 40 200 0 Reference solution Optimal ↓ raw
ms_05_200_180 5 40 200 0 Reference solution Optimal ↓ raw
ms_05_200_199 5 40 200 0 Reference solution Optimal ↓ raw
ms_06_050_001 6 50 50 0 Reference solution Optimal ↓ raw
ms_06_050_002 6 50 50 0 Reference solution Optimal ↓ raw
ms_06_050_003 6 50 50 0 Reference solution Optimal ↓ raw
ms_06_050_004 6 50 50 0 Reference solution Optimal ↓ raw
ms_06_100_002 6 50 100 0 Best submission Optimal ↓ raw
ms_06_100_003 6 50 100 0 Best submission Optimal ↓ raw
ms_06_100_005 6 50 100 0 Best submission Optimal ↓ raw
ms_06_100_010 6 50 100 0 Reference solution Optimal ↓ raw
ms_06_200_077 6 50 200 0 Best submission Optimal ↓ raw
ms_06_200_104 6 50 200 0 Best submission Optimal ↓ raw
ms_06_200_240 6 50 200 0 Best submission Optimal ↓ raw
ms_06_200_289 6 50 200 0 Reference solution Optimal ↓ raw
ms_07_050_001 7 60 50 0 Best submission Optimal ↓ raw
ms_07_050_002 7 60 50 0 Best submission Optimal ↓ raw
ms_07_050_003 7 60 50 0 Best submission Optimal ↓ raw
ms_07_050_004 7 60 50 0 Best submission Optimal ↓ raw
ms_07_100_002 7 60 100 0 Best submission Optimal ↓ raw
ms_07_100_003 7 60 100 0 Best submission Optimal ↓ raw
ms_07_100_005 7 60 100 0 Best submission Optimal ↓ raw
ms_07_100_006 7 60 100 0 Best submission Optimal ↓ raw
ms_07_200_248 7 60 200 0 Best submission Optimal ↓ raw
ms_07_200_370 7 60 200 0 Best submission Optimal ↓ raw
ms_07_200_398 7 60 200 0 Best submission Optimal ↓ raw
ms_07_200_500 7 60 200 0 Best submission Optimal ↓ raw
ms_08_050_000 8 70 50 - - Optimal ↓ raw
ms_08_050_001 8 70 50 - - Optimal ↓ raw
ms_08_050_002 8 70 50 - - Optimal ↓ raw
ms_08_050_003 8 70 50 - - Optimal ↓ raw
ms_08_100_000 8 70 100 - - Optimal ↓ raw
ms_08_100_001 8 70 100 - - Optimal ↓ raw
ms_08_100_002 8 70 100 - - Optimal ↓ raw
ms_08_100_003 8 70 100 - - Optimal ↓ raw
ms_08_200_000 8 70 200 - - Optimal ↓ raw
ms_08_200_001 8 70 200 - - Optimal ↓ raw
ms_08_200_002 8 70 200 - - Optimal ↓ raw
ms_08_200_003 8 70 200 - - Optimal ↓ raw
ms_09_050_000 9 80 50 - - Optimal ↓ raw
ms_09_050_001 9 80 50 - - Optimal ↓ raw
ms_09_050_002 9 80 50 - - Optimal ↓ raw
ms_09_050_003 9 80 50 - - Optimal ↓ raw
ms_09_100_000 9 80 100 - - Optimal ↓ raw
ms_09_100_001 9 80 100 - - Optimal ↓ raw
ms_09_100_002 9 80 100 - - Optimal ↓ raw
ms_09_100_003 9 80 100 - - Optimal ↓ raw
ms_09_200_000 9 80 200 - - Optimal ↓ raw
ms_09_200_001 9 80 200 - - Optimal ↓ raw
ms_09_200_002 9 80 200 - - Optimal ↓ raw
ms_09_200_003 9 80 200 - - Optimal ↓ raw
ms_10_050_000 10 90 50 - - Optimal ↓ raw
ms_10_050_001 10 90 50 - - Optimal ↓ raw
ms_10_050_002 10 90 50 - - Optimal ↓ raw
ms_10_050_003 10 90 50 - - Optimal ↓ raw
ms_10_100_000 10 90 100 - - Optimal ↓ raw
ms_10_100_001 10 90 100 - - Optimal ↓ raw
ms_10_100_002 10 90 100 - - Optimal ↓ raw
ms_10_100_003 10 90 100 - - Optimal ↓ raw
ms_10_200_000 10 90 200 - - Optimal ↓ raw
ms_10_200_001 10 90 200 - - Optimal ↓ raw
ms_10_200_002 10 90 200 - - Optimal ↓ raw
ms_10_200_003 10 90 200 - - Optimal ↓ raw
ms_11_050_000 11 100 50 - - Optimal ↓ raw
ms_11_050_001 11 100 50 - - Optimal ↓ raw
ms_11_050_002 11 100 50 - - Optimal ↓ raw
ms_11_050_003 11 100 50 - - Optimal ↓ raw
ms_11_100_000 11 100 100 - - Optimal ↓ raw
ms_11_100_001 11 100 100 - - Optimal ↓ raw
ms_11_100_002 11 100 100 - - Optimal ↓ raw
ms_11_100_003 11 100 100 - - Optimal ↓ raw
ms_11_200_000 11 100 200 - - Optimal ↓ raw
ms_11_200_001 11 100 200 - - Optimal ↓ raw
ms_11_200_002 11 100 200 - - Optimal ↓ raw
ms_11_200_003 11 100 200 - - Optimal ↓ raw
ms_12_050_000 12 110 50 - - Optimal ↓ raw
ms_12_050_001 12 110 50 - - Optimal ↓ raw
ms_12_050_002 12 110 50 - - Optimal ↓ raw
ms_12_050_003 12 110 50 - - Optimal ↓ raw
ms_12_100_000 12 110 100 - - Optimal ↓ raw
ms_12_100_001 12 110 100 - - Optimal ↓ raw
ms_12_100_002 12 110 100 - - Optimal ↓ raw
ms_12_100_003 12 110 100 - - Open ↓ raw
ms_12_200_000 12 110 200 - - Open ↓ raw
ms_12_200_001 12 110 200 - - Open ↓ raw
ms_12_200_002 12 110 200 - - Open ↓ raw
ms_12_200_003 12 110 200 - - Open ↓ raw
ms_13_050_000 13 120 50 - - Open ↓ raw
ms_13_050_001 13 120 50 - - Open ↓ raw
ms_13_050_002 13 120 50 - - Open ↓ raw
ms_13_050_003 13 120 50 - - Open ↓ raw
ms_13_100_000 13 120 100 - - Open ↓ raw
ms_13_100_001 13 120 100 - - Open ↓ raw
ms_13_100_002 13 120 100 - - Open ↓ raw
ms_13_100_003 13 120 100 - - Open ↓ raw
ms_13_200_000 13 120 200 - - Open ↓ raw
ms_13_200_001 13 120 200 - - Open ↓ raw
ms_13_200_002 13 120 200 - - Open ↓ raw
ms_13_200_003 13 120 200 - - Open ↓ raw
ms_14_050_000 14 130 50 - - Open ↓ raw
ms_14_050_001 14 130 50 - - Open ↓ raw
ms_14_050_002 14 130 50 - - Open ↓ raw
ms_14_050_003 14 130 50 - - Open ↓ raw
ms_14_100_000 14 130 100 - - Open ↓ raw
ms_14_100_001 14 130 100 - - Open ↓ raw
ms_14_100_002 14 130 100 - - Open ↓ raw
ms_14_100_003 14 130 100 - - Open ↓ raw
ms_14_200_000 14 130 200 - - Open ↓ raw
ms_14_200_001 14 130 200 - - Open ↓ raw
ms_14_200_002 14 130 200 - - Open ↓ raw
ms_14_200_003 14 130 200 - - Open ↓ raw
ms_15_050_000 15 140 50 - - Open ↓ raw
ms_15_050_001 15 140 50 - - Open ↓ raw
ms_15_050_002 15 140 50 - - Open ↓ raw
ms_15_050_003 15 140 50 - - Open ↓ raw
ms_15_100_000 15 140 100 - - Open ↓ raw
ms_15_100_001 15 140 100 - - Open ↓ raw
ms_15_100_002 15 140 100 - - Open ↓ raw
ms_15_100_003 15 140 100 - - Open ↓ raw
ms_15_200_000 15 140 200 - - Open ↓ raw
ms_15_200_001 15 140 200 - - Open ↓ raw
ms_15_200_002 15 140 200 - - Open ↓ raw
ms_15_200_003 15 140 200 - - Open ↓ raw