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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 976–1000 of 1277 papers

TitleStatusHype
Neural Packing: from Visual Sensing to Reinforcement Learning—0
Neural Quantum Digital Twins for Optimizing Quantum Annealing—0
Neural Topological Ordering for Computation Graphs—0
Neuromimetic Linear Systems -- Resilience and Learning—0
Neuro-Symbolic Rule Lists—0
New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization—0
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs—0
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem—0
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning—0
Noisy intermediate-scale quantum (NISQ) algorithms—0
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization—0
Noncoherent Massive MIMO with Embedded One-Way Function Physical Layer Security—0
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization—0
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities—0
Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy—0
Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation—0
Note on Combinatorial Engineering Frameworks for Hierarchical Modular Systems—0
Multi-objectivization Inspired Metaheuristics for the Sum-of-the-Parts Combinatorial Optimization Problems—0
NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover—0
Object Detection based on the Collection of Geometric Evidence—0
Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver—0
Offline reinforcement learning for job-shop scheduling problems—0
On Amortizing Inference Cost for Structured Prediction—0
On Approximation Guarantees for Greedy Low Rank Optimization—0
On Circuit Depth Scaling For Quantum Approximate Optimization—0
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