Submitter Variables Constraints Density Status Group Objective MPS File
Toni Sorrell 801 600 8.61423e-02 open supportvectormachine 7438.181167768* gsvm2rl9.mps.gz

Suport vector machine with ramp loss. GSVM2-RL is the formulation found in Hess E. and Brooks P. (2015) paper, The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization

Instance Statistics

Detailed explanation of the following tables can be found here.

Size Related Properties
Original Presolved
Variables 801 801
Constraints 600 600
Binaries 200 200
Integers 0 0
Continuous 601 601
Implicit Integers 0 0
Fixed Variables 0 0
Nonzero Density 0.0861423 0.0861423
Nonzeroes 41400 41400
Constraint Classification Properties
Original Presolved
Total 600 600
Empty 0 0
Free 0 0
Singleton 0 0
Aggregations 0 0
Precedence 200 200
Variable Bound 200 200
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 0 0
Integer Knapsack 0 0
Mixed Binary 200 200
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 2.303196
Constraint % 0.333333 0.333333 0.333333 0.333333
Variable % 0.249688 0.249688 0.249688 0.249688
Score 0.665002

Best Known Solution(s)

No solution available for gsvm2rl9 .

Similar instances in collection

The following instances are most similar to gsvm2rl9 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 Variables Binaries Integers Continuous Constraints Nonz. Submitter Group Status Objective
gsvm2rl11 2001 500 0 1501 1500 253500 Toni Sorrell supportvectormachine open 18121.63800478*
gsvm2rl5 401 100 0 301 300 10700 Toni Sorrell supportvectormachine open 5.4230535252*
gsvm2rl12 2001 500 0 1501 1500 253500 Toni Sorrell supportvectormachine open 22.12011638092*
gsvm2rl3 241 60 0 181 180 4020 Toni Sorrell supportvectormachine easy 0.33652753
neos-619167 3452 400 0 3052 6800 20020 NEOS Server Submission neos-pseudoapplication-83 easy 1.66489361858996

Reference

@article{hess2015support,
  title={The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization},
  author={Hess, Eric J and Brooks, J Paul},
  year={2015}
}

Last Update Nov 19, 2018 by Gregor Hendel
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