Open Access
The SCIP Optimization Suite 7.0
Gerald Gamrath,Daniel Anderson,Ksenia Bestuzheva,Wei-Kun Chen,Leon Eifler,Maxime Gasse,Patrick Gemander,Ambros M. Gleixner,Leona Gottwald,Katrin Halbig,Gregor Hendel,Christopher Hojny,Thorsten Koch,Pierre Le Bodic,Stephen J. Maher,Frederic Matter,Matthias Miltenberger,Erik Muhmer,Benjamin Müller,Marc E. Pfetsch,Franziska Schlösser,Felipe Serrano,Yuji Shinano,Christine Maher Fouad Tawfik,Stefan Vigerske,Fabian Wegscheider,Dieter Weninger,Jakob Witzig +27 more
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TLDR
New features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solverSCIP-SDP, and the parallelization framework UG are described.Abstract:
The SCIP Optimization Suite provides a collection of software packages for
mathematical optimization centered around the constraint integer programming frame-
work SCIP. This paper discusses enhancements and extensions contained in version 7.0
of the SCIP Optimization Suite. The new version features the parallel presolving library
PaPILO as a new addition to the suite. PaPILO 1.0 simplifies mixed-integer linear op-
timization problems and can be used stand-alone or integrated into SCIP via a presolver
plugin. SCIP 7.0 provides additional support for decomposition algorithms. Besides im-
provements in the Benders’ decomposition solver of SCIP, user-defined decomposition
structures can be read, which are used by the automated Benders’ decomposition solver
and two primal heuristics. Additionally, SCIP 7.0 comes with a tree size estimation
that is used to predict the completion of the overall solving process and potentially
trigger restarts. Moreover, substantial performance improvements of the MIP core were
achieved by new developments in presolving, primal heuristics, branching rules, conflict
analysis, and symmetry handling. Last, not least, the report presents updates to other
components and extensions of the SCIP Optimization Suite, in particular, the LP solver
SoPlex and the mixed-integer semidefinite programming solver SCIP-SDP.read more
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Integration of AI and OR techniques in constraint programming for combinatorial optimization problems : first International Conference, CPAIOR 2004, Nice, France, April 20-22, 2004 : proceedings
Jean-Charles Régin,Michel Rueher +1 more
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A review and comparison of solvers for convex MINLP
TL;DR: This paper presents a review of deterministic software for solving convex MINLP problems as well as a comprehensive comparison of a large selection of commonly available solvers, and provides guidelines on how well suited a specific solver or method is for particular types ofMINLP problems.
Journal ArticleDOI
Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning
Hepeng Li,Zhiqiang Wan,Haibo He +2 more
TL;DR: A model-free approach based on safe deep reinforcement learning (SDRL) is proposed to solve the EV charging/discharging scheduling problem as a constrained Markov Decision Process (CMDP) to minimize the charging cost as well as guarantee the EV can be fully charged.
Journal ArticleDOI
Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON
TL;DR: The paper describes BARON's dynamic strategy for deciding under what conditions to activate integer programming relaxations in the course of branch-and-bound, and describes cutting plane and probing techniques that originate from the literature of integer linear programming and have been adapted in BARON to solve nonlinear problems.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
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Reducibility Among Combinatorial Problems
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