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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 12011210 of 1277 papers

TitleStatusHype
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem0
Exploring the Feature Space of TSP Instances Using Quality Diversity0
Extended Deep Submodular Functions0
Fair Disaster Containment via Graph-Cut Problems0
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization0
Faster Matchings via Learned Duals0
Faster quantum mixing for slowly evolving sequences of Markov chains0
Faster width-dependent algorithm for mixed packing and covering LPs0
Fast Hyperparameter Tuning for Ising Machines0
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