istanbul-no-cutoff

benchmark benchmark_suitable aggregations variable_bound knapsack mixed_binary

Submitter Variables Constraints Density Status Group Objective MPS File
Utz-Uwe Haus 5282 20346 6.65103e-04 easy 204.08170701 istanbul-no-cutoff.mps.gz

Exact MILP reformulation using binary decision diagrams to obtain scenario bundles of 2-stage stochastic expected shortest path and expected maximum flow problem with decision dependent scenario probabilities. Notes: * very few binary variables * for each fixing of the binaries a system of equations computing conditioned probabilities remains

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 5282 5110
Constraints 20346 19532
Binaries 30 24
Integers 0 0
Continuous 5252 5086
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.000665103 0.000654744
Nonzeroes 71477 65349
Constraint Classification Properties
Original Presolved
Total 20346 19532
Empty 0 0
Free 0 0
Singleton 144 0
Aggregations 5 5
Precedence 0 0
Variable Bound 0 950
Set Partitioning 0 0
Set Packing 0 0
Set Covering 0 0
Cardinality 0 0
Invariant Knapsack 0 0
Equation Knapsack 0 0
Bin Packing 0 0
Knapsack 1 1
Integer Knapsack 0 0
Mixed Binary 20196 18576
General Linear 0 0
Indicator 0 0

Structure

Available nonzero structure and decomposition information. Further information can be found here.

value min median mean max
Components 1.875061
Constraint % 0.005120 0.0660731 0.0102396 0.875486
Variable % 0.038432 0.1399650 0.0384320 1.383550
Score 0.048552

Best Known Solution(s)

Find solutions below. Download the archive containing all solutions from the Download page.

ID Objective Exact Int. Viol Cons. Viol Obj. Viol Submitter Date Description
1 204.0817 204.0817 3.7e-06 8e-07 0 - 2018-10-13 Solution found during MIPLIB2017 problem selection.

Similar instances in collection

The following instances are most similar to istanbul-no-cutoff in the collection. This similarity analysis is based on 100 scaled instance features describing properties of the variables, objective function, bounds, constraints, and right hand sides.

Instance Status Variables Binaries Integers Continuous Constraints Nonz. Submitter Group Objective Tags
net12 easy 14115 1603 0 12512 14021 80384 P. Belotti 214 benchmark decomposition benchmark_suitable precedence set_packing cardinality invariant_knapsack mixed_binary
snip10x10-35r1budget17 open 47611 63 0 47548 213801 475334 Utz-Uwe Haus 73.82346956113* aggregations variable_bound invariant_knapsack mixed_binary
neos-3759587-noosa easy 27029 4289 0 22740 72104 318169 Jeff Linderoth neos-pseudoapplication-61 48.334467769 benchmark_suitable precedence variable_bound set_partitioning set_packing invariant_knapsack binpacking knapsack mixed_binary
loopha13 easy 19356 18150 0 1206 23758 41809 Hamideh 6.40233 benchmark_suitable aggregations precedence variable_bound invariant_knapsack mixed_binary
neos-3755335-nizao easy 40938 5226 0 35712 111026 547794 Jeff Linderoth neos-pseudoapplication-61 50.0301565326 benchmark_suitable precedence variable_bound set_partitioning set_packing invariant_knapsack binpacking knapsack mixed_binary

Reference

@TechReport{haus-michini-laumanns:17-arxiv,
  author =   {Utz-Uwe Haus and Carla Michini and Marco Laumanns},
  title =    {Scenario Aggregation using Binary Decision Diagrams
                  for Stochastic Programs with Endogenous Uncertainty},
  institution =  {arxiv.org},
  year =     2017,
  type =     {arxiv eprint},
  number =   {arXiv:1701.04055}
}

Last Update Nov 22, 2019 by Gregor Hendel
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