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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 1031–1040 of 1277 papers

TitleStatusHype
Budgeted Influence Maximization for Multiple Products—0
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention—0
Cakewalk Sampling—0
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?—0
Causal Effect Identification in Uncertain Causal Networks—0
Causal Discovery with Reinforcement Learning—0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models—0
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly—0
Characterization of Locality in Spin States and Forced Moves for Optimizations—0
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation—0
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