Search Results for author:
Found 4 papers, 1 papers with code
Learning (Re-)Starting Solutions for Vehicle Routing Problems
A key challenge in solving a combinatorial optimization problem is how to guide the agent (i. e., solver) to efficiently explore the enormous search space.
A Learning-based Iterative Method for Solving Vehicle Routing Problems
This paper is concerned with solving combinatorial optimization problems, in particular, the capacitated vehicle routing problems (CVRP).
Solving Billion-Scale Knapsack Problems
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale.
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent
Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to the heart of leading approaches to reinforcement learning (RL).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
