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https://github.com/chrhansk/scarp

Switching Cost Aware Rounding of Mixed-Integer Optimal Control Problems

https://github.com/chrhansk/scarp

Science Score: 13.0%

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Keywords

optimization-algorithms
Last synced: 5 months ago · JSON representation

Repository

Switching Cost Aware Rounding of Mixed-Integer Optimal Control Problems

Basic Info
  • Host: GitHub
  • Owner: chrhansk
  • License: gpl-2.0
  • Language: C++
  • Default Branch: master
  • Size: 400 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
optimization-algorithms
Created almost 6 years ago · Last pushed 12 months ago
Metadata Files
Readme License

README.md

SCARP

SCARP stands for Switching Cost Aware Rounding. The methods rounds fractional solutions of Mixed-Integer Optimal Control Problems (MIOCPs) to binary ones based on a shortest path through an appropriately defined graph. This implementation supplements the article

Bestehorn, Felix, et al. "Mixed-Integer Optimal Control Problems with switching costs: A shortest path approach."

Dependencies

You will need to satisfy the following dependencies in order to compile SCARP

Build

The following commands build and test SCARP when executed in the root folder of the project:

  • mkdir build
  • cd build
  • cmake ..
  • make
  • make test

Instances

The dataset/ folder contains some test instances, consisting of matrices of fractional controls, which can be rounded using SCARP (or SUR). The matrices are saved as rows of space-separated values. Each row corresponds to one interval of a shooting grid. Consequently, the values of each row sum up to 1 (being based on a one-hot encoding). The (optional) output of the rounding algorithms follow the same convention while being 0/1.

Solvers

There are currently two solvers available, scarp_solver and sur_solver. The options of the solvers can be obtained using: --help

```

scarp_solver --help

Usage: ./src/main/scarpsolver [options] Allowed options: --help produce help message --vanishingconstraints enable vanishing constraints --sur_costs use SUR costs --collect collect all optimal solutions --ampl write output in AMPL format --scale arg (=1) scale factor --input arg input file --output arg output file ```

For example, to solve an instance using SCARP with vanishing constraints, simply use

```

scarpsolver --vanishingconstraints /path/to/instance.dat ```

Owner

  • Name: Christoph Hansknecht
  • Login: chrhansk
  • Kind: user

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