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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 10311040 of 1277 papers

TitleStatusHype
Two-Dimensional Phase Unwrapping via Balanced Spanning Forests0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial OptimizationCode0
An LP-Based Approach for Goal Recognition as Planning0
Design Space Exploration as Quantified Satisfaction0
A new dog learns old tricks: RL finds classic optimization algorithms0
The Cakewalk Method0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach0
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