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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 10511060 of 1277 papers

TitleStatusHype
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization0
Preference Conditioned Neural Multi-objective Combinatorial Optimization0
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation0
Preference Optimization for Combinatorial Optimization Problems0
Primal-dual algorithm for contextual stochastic combinatorial optimization0
Principled Graph Matching Algorithms for Integrating Multiple Data Sources0
Proposed modified computational model for the amoeba-inspired combinatorial optimization machine0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches0
Provable Non-Convex Optimization and Algorithm Validation via Submodularity0
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