What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO
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Publication:5136083
DOI10.1287/ijoc.2017.0798OpenAlexW2897469996MaRDI QIDQ5136083
Swati Gupta, Iain Dunning, John Silberholz
Publication date: 25 November 2020
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://semanticscholar.org/paper/ab4b91cc3339727fe061cad5d23f4d65c30fb1ce
heuristicsmax-cuthyper-heuristicsreproducible researchquadratic unconstrained binary optimizationcomputational testinginterpretable machine learningtest bed
Related Items (15)
Applications and Computational Advances for Solving the QUBO Model ⋮ Metaheuristic Algorithms ⋮ QUBO Software ⋮ On computational capabilities of Ising machines based on nonlinear oscillators ⋮ Discrete dynamical system approaches for Boolean polynomial optimization ⋮ Model-based approaches to multi-attribute diverse matching ⋮ Quantum Annealing versus Digital Computing ⋮ Faster exact solution of sparse maxcut and QUBO problems ⋮ A new global algorithm for max-cut problem with chordal sparsity ⋮ Max-Cut via Kuramoto-Type Oscillators ⋮ A quadratic simplex algorithm for primal optimization over zero-one polytopes ⋮ \texttt{MADAM}: a parallel exact solver for max-cut based on semidefinite programming and ADMM ⋮ Quantum bridge analytics. I: A tutorial on formulating and using QUBO models ⋮ MQLib ⋮ Greedy differencing edge-contraction heuristic for the max-cut problem
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