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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 881–890 of 1277 papers

TitleStatusHype
A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing—0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy—0
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions—0
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution—0
A 2-approximation algorithm for the softwired parsimony problem on binary, tree-child phylogenetic networks—0
A2Perf: Real-World Autonomous Agents Benchmark—0
A Bayesian approach for prompt optimization in pre-trained language models—0
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching—0
A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration—0
A case study of algorithm selection for the traveling thief problem—0
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