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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

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Showing 311–320 of 1277 papers

TitleStatusHype
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization—0
Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—0
Multi-task Representation Learning for Mixed Integer Linear ProgrammingCode0
LLMs for Cold-Start Cutting Plane Separator ConfigurationCode0
Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks—0
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization—0
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