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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 9911000 of 1277 papers

TitleStatusHype
Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation0
Note on Combinatorial Engineering Frameworks for Hierarchical Modular Systems0
Multi-objectivization Inspired Metaheuristics for the Sum-of-the-Parts Combinatorial Optimization Problems0
NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover0
Object Detection based on the Collection of Geometric Evidence0
Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver0
Offline reinforcement learning for job-shop scheduling problems0
On Amortizing Inference Cost for Structured Prediction0
On Approximation Guarantees for Greedy Low Rank Optimization0
On Circuit Depth Scaling For Quantum Approximate Optimization0
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