An Easy Way to Build Parallel State-of-the-art Combinatorial Optimization Problem Solvers: A Computational Study on Solving Steiner Tree Problems and Mixed Integer Semidefinite Programs by using ug[SCIP-*,*]-Libraries

Shinano, Yuji; Rehfeldt, Daniel; Gally, Tristan

doi:10.1109/ipdpsw.2019.00095

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…We have developed parallel solvers for SCIP [58,61,59], CPLEX (not developed anymore), FICO Xpress [60], PIPS-SBB [44,45], Concorde 5 , and QapNB [18]. Customized SCIP-based solvers such as SCIP-SDP and SCIP-Jack can be parallelized with minimal effort [62]. The parallel version of SCIP-Jack solved several previously unsolved instances from SteinLib [35] by using up to 43,000 cores [63].…”

Section: The Ug Frameworkmentioning

confidence: 99%

Enabling Research through the SCIP Optimization Suite 8.0

Bestuzheva

Besançon

Chen

et al. 2023

ACM Trans. Math. Softw.

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The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP . The focus of this paper is on the role of the SCIP Optimization Suite in supporting research. SCIP ’s main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP ’s application as a research tool and as a platform for further developments. Further, the paper gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP .

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Section: The Ug Frameworkmentioning

confidence: 99%

Enabling Research through the SCIP Optimization Suite 8.0

Bestuzheva

Besançon

Chen

et al. 2023

ACM Trans. Math. Softw.

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“…More details are given in [62,122,123]. A parallel version of SCIP-SDP is also available, see [149].…”

Section: Solving Mixed-integer Semidefinite Programsmentioning

confidence: 99%

Strategies for Mastering Uncertainty

Pfetsch

Abele

Altherr

et al. 2021

Springer Tracts in Mechanical Engineering

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This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.

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The Impact and Implications of Optimization

Kallrath

2021

International Series in Operations Research &Amp; Management Science

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An Easy Way to Build Parallel State-of-the-art Combinatorial Optimization Problem Solvers: A Computational Study on Solving Steiner Tree Problems and Mixed Integer Semidefinite Programs by using ug[SCIP-,]-Libraries

Cited by 6 publications

References 40 publications

Enabling Research through the SCIP Optimization Suite 8.0

Enabling Research through the SCIP Optimization Suite 8.0

Strategies for Mastering Uncertainty

The Impact and Implications of Optimization

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