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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 571580 of 1277 papers

TitleStatusHype
Solving the vehicle routing problem with deep reinforcement learning0
Analysis of Quality Diversity Algorithms for the Knapsack Problem0
Learning with Combinatorial Optimization Layers: a Probabilistic ApproachCode1
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System0
Annealed Training for Combinatorial Optimization on Graphs0
Differentially Private Partial Set Cover with Applications to Facility Location0
Bayesian Optimization for Macro Placement0
Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization ProblemsCode0
Neural Topological Ordering for Computation Graphs0
Unsupervised Learning for Combinatorial Optimization with Principled Objective RelaxationCode1
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