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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
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing0
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning0
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching0
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions0
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization0
Dynamic Algorithms for Matroid Submodular Maximization0
D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis0
Duality between Feature Selection and Data Clustering0
Bayesian preference elicitation for multiobjective combinatorial optimization0
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