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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 641650 of 1277 papers

TitleStatusHype
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
A Survey for Solving Mixed Integer Programming via Machine Learning0
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and InsightsCode1
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes0
Noncoherent Massive MIMO with Embedded One-Way Function Physical Layer Security0
Reinforcement Learning in Practice: Opportunities and Challenges0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing0
Evolutionary Construction of Perfectly Balanced Boolean Functions0
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