# Combinatorial Optimization

111 papers with code • 0 benchmarks • 1 datasets

**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.

# Benchmarks

# Greatest papers with code

# ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution

We consider the problem of efficient blackbox optimization over a large hybrid search space, consisting of a mixture of a high dimensional continuous space and a complex combinatorial space.

# Fair Correlation Clustering

We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints.

# Pointer Networks

It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output.

# Attention, Learn to Solve Routing Problems!

The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development.

# A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network

Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.

Combinatorial Optimization Multi-agent Reinforcement Learning

# Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching

We also show how to extend our network to hypergraph matching, and matching of multiple graphs.

Ranked #1 on Graph Matching on PASCAL VOC

# Learning Combinatorial Optimization Algorithms over Graphs

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

# Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.

# Causal Discovery with Reinforcement Learning

The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.

# Reinforcement Learning for Solving the Vehicle Routing Problem

Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance.