Publications related to Tensor train for global optimization problems in robotics

Related publications (7)

Bandit Pareto Set Identification with Posterior Sampling

The problem of identifying the best answer among a collection of items having real-valued distribution is well-understood. Despite its practical relevance for many applications, fewer works have studied its extension when multiple and potentially conflict ...

PMLR2025

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Giovanni Piccioli

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

EPFL2024

GRAPH-STRUCTURED TENSOR OPTIMIZATION FOR NONLINEAR DENSITY CONTROL AND MEAN FIELD GAMES

Isabel Haasler

In this work we develop a numerical method for solving a type of convex graph-structured tensor optimization problem. This type of problem, which can be seen as a generalization of multimarginal optimal transport problems with graph-structured costs, appea ...

2024

A methodology to optimize complex models in the context of nuclear waste repositories

Lyesse Laloui, Matthias Timothee Stanislas Wojnarowicz

With the increase in computational capabilities in recent decades, the possibility of modeling complex phenomena has continued to grow, inevitably increasing the number of calibrated parameters. Determining these parameters for complex coupled models can b ...

2024

Optimizing Two-Truck Platooning With Deadlines

Wenjie Xu

We study a transportation problem where two heavy-duty trucks travel across the national highway from separate origins to destinations, subject to individual deadline constraints. Our objective is to minimize their total fuel consumption by jointly optimiz ...

IEEE Institute of Electrical and Electronics Engineers2023

Approximation of Lipschitz Functions Using Deep Spline Neural Networks

Michaël Unser, Alexis Marie Frederic Goujon, Pakshal Narendra Bohra, Sebastian Jonas Neumayer

Although Lipschitz-constrained neural networks have many applications in machine learning, the design and training of expressive Lipschitz-constrained networks is very challenging. Since the popular rectified linear-unit networks have provable disadvantage ...

2023

Can tournament selection improve performances of the classical particle swarm optimization algorithm?

Juan Ramon Mosig, Ivica Stevanovic, Ruzica Golubovic

2009