A review and comparison of solvers for convex MINLP
In this paper, we present a review of deterministic software for solving convex MINLP
problems as well as a comprehensive comparison of a large selection of commonly …
problems as well as a comprehensive comparison of a large selection of commonly …
Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning
DR Morrison, SH Jacobson, JJ Sauppe, EC Sewell - Discrete Optimization, 2016 - Elsevier
The branch-and-bound (B&B) algorithmic framework has been used successfully to find
exact solutions for a wide array of optimization problems. B&B uses a tree search strategy to …
exact solutions for a wide array of optimization problems. B&B uses a tree search strategy to …
The SCIP optimization suite 8.0
K Bestuzheva, M Besançon, WK Chen… - arXiv preprint arXiv …, 2021 - arxiv.org
The SCIP Optimization Suite provides a collection of software packages for mathematical
optimization centered around the constraint integer programming framework SCIP. This …
optimization centered around the constraint integer programming framework SCIP. This …
Solving mixed integer programs using neural networks
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …
developed with decades of research to solve large-scale MIP instances encountered in …
Exact combinatorial optimization with graph convolutional neural networks
Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …
paradigm. We propose a new graph convolutional neural network model for learning branch …
[图书][B] Integer programming models
M Conforti, G Cornuéjols, G Zambelli, M Conforti… - 2014 - Springer
The importance of integer programming stems from the fact that it can be used to model a
vast array of problems arising from the most disparate areas, ranging from practical ones …
vast array of problems arising from the most disparate areas, ranging from practical ones …
Learning to branch in mixed integer programming
The design of strategies for branching in Mixed Integer Programming (MIP) is guided by
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …
Hybrid models for learning to branch
Abstract A recent Graph Neural Network (GNN) approach for learning to branch has been
shown to successfully reduce the running time of branch-and-bound algorithms for Mixed …
shown to successfully reduce the running time of branch-and-bound algorithms for Mixed …
[HTML][HTML] A modeler's guide to handle complexity in energy systems optimization
Determining environmentally-and economically-optimal energy systems designs and
operations is complex. In particular, the integration of weather-dependent renewable energy …
operations is complex. In particular, the integration of weather-dependent renewable energy …
Mixed-integer nonlinear optimization
Many optimal decision problems in scientific, engineering, and public sector applications
involve both discrete decisions and nonlinear system dynamics that affect the quality of the …
involve both discrete decisions and nonlinear system dynamics that affect the quality of the …