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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 1021–1030 of 1277 papers

TitleStatusHype
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction—0
Submodular Batch Selection for Training Deep Neural NetworksCode0
Reinforcement Learning Driven Heuristic Optimization—0
Curriculum Learning for Cumulative Return MaximizationCode0
Causal Discovery with Reinforcement Learning—0
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem—0
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process—0
Combinatorial Persistency Criteria for Multicut and Max-Cut—0
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