The SCIP Optimization Suite 7.0

By K. Bestuzheva, Mathieu Besanccon, Weikun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, L. Eifler, Oliver Gaul, Gerald Gamrath, Ambros M. Gleixner and 25 more. 2020

650 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bc43b4164b720cff94bc4db28c825fdc79c15d39

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Citations in the corpus

Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2019, result): “We compare against the previously proposed approaches of Khalil et al. [30], Alvarez et al. [4] and Hansknecht et al. [24], as well as against the default hybrid branching rule in SCIP [20], a modern open-source solver.”
CAQL: Continuous Action Q-Learning (2019, methodology): “, SCIP (Gleixner et al., 2018), CPLEX (CPLEX, 2019), Gurobi (Gurobi, 2019)).”
Reinforcement Learning for Combinatorial Optimization: A Survey (2020, background): “l Auction, Capacitated Facility Location, and Maximum Independent Set can be reformulated as a special case of a mixed-integer linear program. Several commercial optimizers such as CPLEX [19] or SCIP [20] exist that use branch-and-bound and other routines to solve the mixed-integer linear program. D”
IPBoost - Non-Convex Boosting via Integer Programming (2020, methodology): “The latter reports that integer programming solving performance has seen a combined hardware and software speed-up of 80 billion from 1991 to 2015 (hardware: 570 000, software 1 400 000) using state-of-the-art hardware and solvers such as CPLEX (see [12]), Gurobi (see [25]), XPRESS (see [36]), and S”
MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library (2021, background): “Nevertheless, GCG, DIP, and DECOMP are all able to derive information about the block structure automatically (see, e.g., [23, Sec.”
Combinatorial optimization and reasoning with graph neural networks (2021, methodology): “It is also based on OpenAI Gym, and it exposes several essential decision tasks arising in general-purpose CO solvers—such as SCIP [45]—as control problems over Markov decision processes.”
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs (2019, unknown): “Given that the agent can be trained offline, the algorithm provided solutions much faster for some classes of problems compared to an open-source general-purpose mixedinteger programming (MIP) solver, in their case, SCIP [9, 10].”