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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 581–590 of 1277 papers

TitleStatusHype
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms—0
Bayesian Optimization for Macro Placement—0
Batch Active Learning via Coordinated Matching—0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem—0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize—0
Diversity from Human Feedback—0
Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~schuetz2022combinatorial,angelini2023modern,schuetz2023reply—0
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm—0
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
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