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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 511520 of 1277 papers

TitleStatusHype
Local Branching Relaxation Heuristics for Integer Linear Programs0
Learning to repeatedly solve routing problems0
Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach0
Walkability Optimization: Formulations, Algorithms, and a Case Study of TorontoCode0
Efficient Optimization with Higher-Order Ising Machines0
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural NetworksCode0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems0
Fast Hyperparameter Tuning for Ising Machines0
Reinforced Genetic Algorithm for Structure-based Drug DesignCode1
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls0
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