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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

Showing 511520 of 1277 papers

TitleStatusHype
Deep Auto-Deferring Policy for Combinatorial Optimization0
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy0
Bayesian Meta-Prior Learning Using Empirical Bayes0
A Survey on Reinforcement Learning for Combinatorial Optimization0
A Local Optima Network View of Real Function Fitness Landscapes0
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem0
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization0
Decision-focused Graph Neural Networks for Combinatorial Optimization0
Graph Ordering: Towards the Optimal by Learning0
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization0
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