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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 291300 of 1277 papers

TitleStatusHype
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem0
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer0
Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem0
Bayesian preference elicitation for multiobjective combinatorial optimization0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem0
Accelerating Matroid Optimization through Fast Imprecise Oracles0
Bayesian Optimization for Macro Placement0
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