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Related papers: Basis Pursuit Denoise with Nonsmooth Constraints

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We present the first experimental validation of the Deep Lie Map Network (DLMN) approach for recovering both linear and non-linear optics in a synchrotron. The DLMN facilitates the construction of a detailed accelerator model by integrating…

Accelerator Physics · Physics 2024-08-22 Conrad Caliari , Adrian Oeftiger , Oliver Boine-Frankenheim

To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous shooting acquisitions, where seismic sources are fired at…

Geophysics · Physics 2022-06-01 Nick Luiken , Matteo Ravasi , Claire E. Birnie

We present a novel method for training score-based generative models which uses nonlinear noising dynamics to improve learning of structured distributions. Generalizing to a nonlinear drift allows for additional structure to be incorporated…

Machine Learning · Statistics 2025-07-10 Jeremiah Birrell , Markos A. Katsoulakis , Luc Rey-Bellet , Benjamin J. Zhang , Wei Zhu

In large-scale, data-driven applications, parameters are often only known approximately due to noise and limited data samples. In this paper, we focus on high-dimensional optimization problems with linear constraints under uncertain…

Optimization and Control · Mathematics 2024-03-01 Naqi Huang , Nestor Parolya , Theresia van Essen

We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lower semi-continuous and prox-bounded. We combine an adaptive…

Machine Learning · Statistics 2022-06-20 Dounia Lakhmiri , Dominique Orban , Andrea Lodi

A nonsmooth set-gradient ascent method is developed for moving finite approximation sets toward the Pareto front in multiobjective optimization. The method optimizes layered set indicators: a base indicator is evaluated on successive…

Optimization and Control · Mathematics 2026-05-14 Michael T. M. Emmerich

We consider the problem of minimization of a convex function on a simple set with convex non-smooth inequality constraint and describe first-order methods to solve such problems in different situations: smooth or non-smooth objective…

Optimization and Control · Mathematics 2018-01-30 Anastasia Bayandina , Pavel Dvurechensky , Alexander Gasnikov , Fedor Stonyakin , Alexander Titov

Recently, decentralized optimization over the Stiefel manifold has attacked tremendous attentions due to its wide range of applications in various fields. Existing methods rely on the gradients to update variables, which are not applicable…

Optimization and Control · Mathematics 2024-01-09 Lei Wang , Xin Liu

We consider sparsity-based techniques for the approximation of high-dimensional functions from random pointwise evaluations. To date, almost all the works published in this field contain some a priori assumptions about the error corrupting…

Numerical Analysis · Mathematics 2019-05-10 Ben Adcock , Anyi Bao , Simone Brugiapaglia

The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science. Smooth formulations of the problem often exhibit an undesirable phenomenon: the condition number, classically…

Optimization and Control · Mathematics 2019-04-24 Vasileios Charisopoulos , Yudong Chen , Damek Davis , Mateo Díaz , Lijun Ding , Dmitriy Drusvyatskiy

Tuning hyperparameters, such as the stepsize, presents a major challenge of training machine learning models. To address this challenge, numerous adaptive optimization algorithms have been developed that achieve near-optimal complexities,…

Optimization and Control · Mathematics 2023-11-07 Florian Hübler , Junchi Yang , Xiang Li , Niao He

Deploying deep neural networks (DNNs) on edge devices requires strong compression with minimal accuracy loss. This paper introduces Mix-and-Match Pruning, a globally guided, layer-wise sparsification framework that leverages sensitivity…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Danial Monachan , Samira Nazari , Mahdi Taheri , Ali Azarpeyvand , Milos Krstic , Michael Huebner , Christian Herglotz

In this paper, we consider the unconstrained multiobjective optimization problem. In recent years, researchers pointed out that the steepest decent method may generate small stepsize which leads to slow convergence rates. To address the…

Optimization and Control · Mathematics 2025-12-12 Yingxue Yang

Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance…

Machine Learning · Computer Science 2021-06-14 Sebastian Ament , Carla Gomes

The Muon optimizer has recently demonstrated remarkable empirical success in training large language models. However, the theoretical understanding of its mechanisms remains limited. Current convergence guarantees for Muon rely heavily on…

Machine Learning · Computer Science 2026-05-27 Yixuan Yang , Yuqing He , Song Li

It has been observed in a variety of contexts that gradient descent methods have great success in solving low-rank matrix factorization problems, despite the relevant problem formulation being non-convex. We tackle a particular instance of…

Numerical Analysis · Computer Science 2016-06-28 Dejiao Zhang , Laura Balzano

This paper investigates the applicability of a recently-proposed nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework…

Computational Engineering, Finance, and Science · Computer Science 2022-10-24 Rimple Sandhu , Brandon Robinson , Mohammad Khalil , Chris L. Pettit , Dominique Poirel , Abhijit Sarkar

We study the training objectives of denoising-based generative models, with a particular focus on loss weighting and output parameterization, including noise-, clean image-, and velocity-based formulations. Through a systematic numerical…

Computer Vision and Pattern Recognition · Computer Science 2026-04-20 Anne Gagneux , Ségolène Martin , Rémi Gribonval , Mathurin Massias

In this paper, we analyze the accuracy of gradient estimates obtained by linear interpolation when the underlying function is subject to bounded measurement noise. The total gradient error is decomposed into a deterministic component…

Numerical Analysis · Mathematics 2025-07-29 Alejandro G. Marchetti , Dominique Bonvin

Diffusion models offer powerful generative capabilities for robot trajectory planning, yet their practical deployment on robots is hindered by a critical bottleneck: a reliance on imitation learning from expert demonstrations. This paradigm…

Robotics · Computer Science 2026-02-25 Lei Ye , Haibo Gao , Peng Xu , Zhelin Zhang , Junqi Shan , Ao Zhang , Wei Zhang , Ruyi Zhou , Zongquan Deng , Liang Ding