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Combinatorial Optimization

27 papers with code · Methodology

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Pointer Networks

NeurIPS 2015 PaddlePaddle/models

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.

COMBINATORIAL OPTIMIZATION

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

NeurIPS 2018 crazydonkey200/neural-symbolic-machines

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.

COMBINATORIAL OPTIMIZATION PROGRAM SYNTHESIS SEMANTIC PARSING STRUCTURED PREDICTION

Attention, Learn to Solve Routing Problems!

ICLR 2019 wouterkool/attention-learn-to-route

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

COMBINATORIAL OPTIMIZATION

Learning Combinatorial Optimization Algorithms over Graphs

NeurIPS 2017 Hanjun-Dai/graph_comb_opt

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

COMBINATORIAL OPTIMIZATION GRAPH EMBEDDING

Reinforcement Learning for Solving the Vehicle Routing Problem

NeurIPS 2018 OptMLGroup/VRP-RL

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.

COMBINATORIAL OPTIMIZATION

Neural Combinatorial Optimization with Reinforcement Learning

29 Nov 2016MichelDeudon/neural-combinatorial-optimization-rl-tensorflow

Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

COMBINATORIAL OPTIMIZATION

Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

5 Mar 2018hazimehh/L0Learn

While these methods lead to estimators with excellent statistical properties, often there is a price to pay in terms of a steep increase in computation times, especially when compared to highly efficient popular algorithms for sparse learning (e. g., based on $L_1$-regularization) that scale to much larger problem sizes.

COMBINATORIAL OPTIMIZATION SPARSE LEARNING

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

4 Jun 2019ds4dm/learn2branch

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.

COMBINATORIAL OPTIMIZATION IMITATION LEARNING

Deep Learning as a Mixed Convex-Combinatorial Optimization Problem

ICLR 2018 afriesen/ftprop

Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case.

COMBINATORIAL OPTIMIZATION QUANTIZATION