SOTAVerified

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 981990 of 1277 papers

TitleStatusHype
New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization0
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs0
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem0
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning0
Noisy intermediate-scale quantum (NISQ) algorithms0
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization0
Noncoherent Massive MIMO with Embedded One-Way Function Physical Layer Security0
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization0
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities0
Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy0
Show:102550
← PrevPage 99 of 128Next →

No leaderboard results yet.