Search
Search Results
-
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine...
-
Multi-task Predict-then-Optimize
The predict-then-optimize framework arises in a wide variety of applications where the unknown cost coefficients of an optimization problem are first...
-
Learning and Intelligent Optimization 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers
This book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice,...
-
Learning to select the recombination operator for derivative-free optimization
Extensive studies on selecting recombination operators adaptively, namely, adaptive operator selection (AOS), during the search process of an...
-
Machine Learning to Control Network Powered by Computing Infrastructure
AbstractMachine learning (ML) methods are applied to optimal resource control for Network Powered by Computing Infrastructure (NPC)—a new generation...
-
Predict, Tune and Optimize for Data-Driven Shift Scheduling with Uncertain Demands
When it comes to data-driven optimization under uncertainty, it is well known that a naïve predict-then-optimize pipeline in which point forecasts...
-
Learning to project in a criterion space search algorithm: an application to multi-objective binary linear programming
In this paper, we investigate the possibility of improving the performance of multi-objective optimization solution approaches using machine learning...
-
A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack
In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement...
-
Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search
Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine...
-
Deep Metric Learning: Loss Functions Comparison
AbstractAn overview of deep metric learning methods is presented. Although they have appeared in recent years, these methods were compared only with...
-
Flexible job-shop scheduling with limited flexible workers using an improved multiobjective discrete teaching–learning based optimization algorithm
Flexible job-shop scheduling problem with worker flexibility (FJSPW) has been frequently investigated during the last decade. Many real-world...
-
A deep learning-based approach to a newsvendor problem considering uncertainty and time-varying costs
We study a newsvendor problem in which demand is uncertain and newsvendor costs are highly time-varying. In this work, we propose a deep learning...
-
Multimodal Deep Learning
Multimodal deep learning has gained significant attention and shown great promise in various domains, including medical, manufacturing, Internet of...
-
Application of machine learning methods to predict drought cost in France
This paper addresses the prediction of the total damage costs brought on by a drought episode under the French “Régime de Catastrophes Naturelles”....
-
Learning unbounded-domain spatiotemporal differential equations using adaptive spectral methods
Rapidly develo** machine learning methods have stimulated research interest in computationally reconstructing differential equations (DEs) from...
-
Introduction to Optimal Control and Reinforcement Learning
This chapter presents the motivation of develo** reinforcement learning algorithms for solving optimal control problems of dynamical systems. An...
-
Global Convergence in Learning Fully-Connected ReLU Networks Via Un-rectifying Based on the Augmented Lagrangian Approach
Most learning algorithms for deep neural networks (DNNs) employ gradient descent or block coordinate descent, which involves the direct application...
-
An integrated learning and progressive hedging matheuristic for stochastic network design problem
We address the Multicommodity Capacitated Fixed-charge Network Design problem with uncertain demands , which we formulate as a two-stage stochastic...
-
Hierarchical Method for Cooperative Multiagent Reinforcement Learning in Markov Decision Processes
AbstractIn the rapidly evolving field of reinforcement learning, combination of hierarchical and multiagent learning methods presents unique...
-
Machine Learning
Machine learning is an area of artificial intelligence that aims to develop systems that can learn and improve from data. The central concept of...