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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 12611270 of 1277 papers

TitleStatusHype
The Lovász ϑ function, SVMs and finding large dense subgraphs0
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP0
On Amortizing Inference Cost for Structured Prediction0
Batch Active Learning via Coordinated Matching0
A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics0
Implicitly Intersecting Weighted Automata using Dual Decomposition0
Optimized Online Rank Learning for Machine Translation0
Regret in Online Combinatorial OptimizationCode0
Robust Metric Learning by Smooth Optimization0
A Polynomial Time Approximation Scheme for a Single Machine Scheduling Problem Using a Hybrid Evolutionary Algorithm0
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