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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 10211030 of 1277 papers

TitleStatusHype
Optimization Networks for Integrated Machine Learning0
Optimized Online Rank Learning for Machine Translation0
Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections0
Optimizing Tensor Network Contraction Using Reinforcement Learning0
optimizn: a Python Library for Developing Customized Optimization Algorithms0
Oracle Efficient Algorithms for Groupwise Regret0
PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching0
Parallel Genetic Algorithm to Solve Traveling Salesman Problem on MapReduce Framework using Hadoop Cluster0
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization0
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity0
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