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The SCIP Optimization Suite 10.0

Christopher Hojny, Mathieu Besançon, Ksenia Bestuzheva, Sander Borst, João Dionísio, Johannes Ehls, Leon Eifler, Mohammed Ghannam, Ambros Gleixner, Adrian Göß, Alexander Hoen, Jacob von Holly-Ponientzietz, Rolf van der Hulst, Dominik Kamp, Thorsten Koch, Kevin Kofler, Jurgen Lentz, Marco Lübbecke, Stephen J. Maher, Paul Matti Meinhold, Gioni Mexi, Til Mohr, Erik Mühmer, Krunal Kishor Patel, Marc E. Pfetsch, Sebastian Pokutta, Chantal Reinartz Groba, Felipe Serrano, Yuji Shinano, Mark Turner, Stefan Vigerske, Matthias Walter, Dieter Weninger, Liding Xu

TL;DR

SCIP The SCIP Optimization Suite 10.0 advances exact rational solving for MILPs, enhances presolving with implied integrality detection, broadens symmetry handling to include reflection symmetries, and introduces cut-based conflict analysis and a flower separator. It also adds two new primal heuristics, a feasible-explanation tool, a nonlinear solver interface, and expanded Benders and decomposition capabilities, complemented by updates to PaPILO, UG, GCG, and the MIP-DD delta debugger. Empirical results show improved performance and reliability for MILP and MINLP across diverse benchmarks, with notable gains on harder instances and greater robustness due to bug fixes and feature enrichments. Together, these developments strengthen SCIP's solver ecosystem, enabling more scalable, verifiable, and versatile optimization across a wide range of problem classes and integrations with external solvers and interfaces.

Abstract

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in SCIP Optimization Suite 10.0. The updates in SCIP 10.0 include a new solving mode for exactly solving rational mixed-integer linear programs, a new presolver for detecting implied integral variables, a novel cut-based conflict analysis and separator for flower inequalities, two new heuristics, a novel tool for explaining infeasibility, a new interface for nonlinear solvers as well as improvements in symmetry handling, branching strategies, and SCIP's Benders' decomposition framework. SCIP Optimization Suite 10.0 also includes new and improved features in the the presolving library PaPILO, the parallel framework UG, and the decomposition framework GCG. Moreover, the SCIP Optimization Suite 10.0 contains MIP-DD, the first open-source delta debugger for mixed-integer programming solvers. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.

The SCIP Optimization Suite 10.0

TL;DR

SCIP The SCIP Optimization Suite 10.0 advances exact rational solving for MILPs, enhances presolving with implied integrality detection, broadens symmetry handling to include reflection symmetries, and introduces cut-based conflict analysis and a flower separator. It also adds two new primal heuristics, a feasible-explanation tool, a nonlinear solver interface, and expanded Benders and decomposition capabilities, complemented by updates to PaPILO, UG, GCG, and the MIP-DD delta debugger. Empirical results show improved performance and reliability for MILP and MINLP across diverse benchmarks, with notable gains on harder instances and greater robustness due to bug fixes and feature enrichments. Together, these developments strengthen SCIP's solver ecosystem, enabling more scalable, verifiable, and versatile optimization across a wide range of problem classes and integrations with external solvers and interfaces.

Abstract

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in SCIP Optimization Suite 10.0. The updates in SCIP 10.0 include a new solving mode for exactly solving rational mixed-integer linear programs, a new presolver for detecting implied integral variables, a novel cut-based conflict analysis and separator for flower inequalities, two new heuristics, a novel tool for explaining infeasibility, a new interface for nonlinear solvers as well as improvements in symmetry handling, branching strategies, and SCIP's Benders' decomposition framework. SCIP Optimization Suite 10.0 also includes new and improved features in the the presolving library PaPILO, the parallel framework UG, and the decomposition framework GCG. Moreover, the SCIP Optimization Suite 10.0 contains MIP-DD, the first open-source delta debugger for mixed-integer programming solvers. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.

Paper Structure

This paper contains 76 sections, 15 equations, 1 figure, 8 tables.

Table of Contents

  1. Abstract
  2. Keywords
  3. Mathematics Subject Classification
  4. Introduction
  5. New Developments and Structure of the Paper
  6. Background
  7. Overall Performance Improvements for MILP and MINLP with SCIP
  8. Experimental Setup
  9. Performance
  10. Performance
  11. SCIP
  12. A Numerically Exact Solving Mode for MILPs
  13. Exact Instance Readers and a Wrapper for Rational Numbers
  14. Exact Variable Data and a New Handler for Exact Linear Constraints
  15. Exact Presolving using
  16. ...and 61 more sections

Figures (1)

  • Figure 1: Left: Region defined by $C_\texttt{reason}$, $C_\texttt{conf}$, and the global domain. Middle: Mixed-integer hull, where x1 is the only integer variable. Right: Mixed-integer hull intersected with the local domain. In particular, two integer-feasible points (leftmost and rightmost red points) and a convex combination of them that intersects the local domain (middle red point).