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This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions…

Optimization and Control · Mathematics 2024-02-12 Zhong Zheng , Shiqian Ma , Lingzhou Xue

Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario subproblems have to be solved at each iteration. In this…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-09-28 Gilles Bareilles , Yassine Laguel , Dmitry Grishchenko , Franck Iutzeler , Jérôme Malick

Federated learning (FL), which has gained increasing attention recently, enables distributed devices to train a common machine learning (ML) model for intelligent inference cooperatively without data sharing. However, problems in practical…

Machine Learning · Computer Science 2022-11-01 Yujie Zhou , Zhidu Li , Tong Tang , Ruyan Wang

With rapid advancements in machine learning, first-order algorithms have emerged as the backbone of modern optimization techniques, owing to their computational efficiency and low memory requirements. Recently, the connection between…

Quantum Physics · Physics 2025-05-21 Jiaqi Leng , Bin Shi

Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning. Usually, binary optimization problems are…

Optimization and Control · Mathematics 2021-05-18 Huan Xiong , Mengyang Yu , Li Liu , Fan Zhu , Fumin Shen , Ling Shao

This paper considers the problem of real-time mode scheduling in linear time-varying switched systems subject to a quadratic cost functional. The execution time of hybrid control algorithms is often prohibitive for real-time applications…

Optimization and Control · Mathematics 2017-09-04 Anastasia Mavrommati , Jarvis A. Schultz , Todd D. Murphey

Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and approximate optimal control. By leveraging time-variant local…

Systems and Control · Electrical Eng. & Systems 2019-08-01 Onur Celik , Hany Abdulsamad , Jan Peters

A framework is introduced for sequentially solving convex stochastic minimization problems, where the objective functions change slowly, in the sense that the distance between successive minimizers is bounded. The minimization problems are…

Optimization and Control · Mathematics 2018-03-12 Craig Wilson , Venugopal Veeravalli , Angelia Nedich

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an…

Local Fourier analysis is a useful tool for predicting and analyzing the performance of many efficient algorithms for the solution of discretized PDEs, such as multigrid and domain decomposition methods. The crucial aspect of local Fourier…

Optimization and Control · Mathematics 2020-07-29 Jed Brown , Yunhui He , Scott MacLachlan , Matt Menickelly , Stefan M. Wild

The problem of steering a particular class of $n$-dimensional continuous-time dynamical systems towards the minima of a function without gradient information is considered. We propose an hybrid controller, implementing a discrete-time…

Systems and Control · Electrical Eng. & Systems 2019-12-05 Alessandro Melis , Ricardo G. Sanfelice , Lorenzo Marconi

We study the problem of automatically computing the controllable region of a Linear Hybrid Automaton, with respect to a safety objective. We describe the techniques that are needed to effectively and efficiently implement a…

Logic in Computer Science · Computer Science 2011-06-08 Massimo Benerecetti , Marco Faella , Stefano Minopoli

This paper addresses the challenging numerical simulation of nonlinear hybrid stochastic functional differential equations with infinite delays. We first propose an explicit scheme using space and time truncation, requiring only finite…

Numerical Analysis · Mathematics 2025-12-23 Guozhen Li , Xiaoyue Li , Xuerong Mao

In this paper we study simulation based optimization algorithms for solving discrete time optimal stopping problems. This type of algorithms became popular among practioneers working in the area of quantitative finance. Using large…

Optimization and Control · Mathematics 2009-09-22 Denis Belomestny

The verification and validation of automated and autonomous driving systems impose a major challenge, especially the identification of suitable test scenarios. This work presents a methodology that adopts metaheuristic search to optimize…

Software Engineering · Computer Science 2019-01-18 Florian Hauer , Bernd Holzmüller

We propose a variant of the classical conditional gradient method for sparse inverse problems with differentiable measurement models. Such models arise in many practical problems including superresolution, time-series modeling, and matrix…

Optimization and Control · Mathematics 2015-07-07 Nicholas Boyd , Geoffrey Schiebinger , Benjamin Recht

Testing controllers in safety-critical systems is vital for ensuring their safety and preventing failures. In this paper, we address the falsification problem within learning-based closed-loop control systems through simulation. This…

Systems and Control · Electrical Eng. & Systems 2024-09-13 Zahra Shahrooei , Mykel J. Kochenderfer , Ali Baheri

Nonlinear optimal control problems for trajectory planning with obstacle avoidance present several challenges. While general-purpose optimizers and dynamic programming methods struggle when adopted separately, their combination enabled by a…

Optimization and Control · Mathematics 2024-08-09 Rebecca Richter , Alberto De Marchi , Matthias Gerdts

Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite programs. We examine two such representative problems and show that they can be formulated in a similar manner. The…

Optimization and Control · Mathematics 2019-12-30 Armin Zare , Hesameddin Mohammadi , Neil K. Dhingra , Tryphon T. Georgiou , Mihailo R. Jovanović

This work proposes a hybrid modeling framework based on recurrent neural networks (RNNs) and the finite element (FE) method to approximate model discrepancies in time dependent, multi-fidelity problems, and use the trained hybrid models to…

Computational Engineering, Finance, and Science · Computer Science 2024-02-20 Moritz von Tresckow , Herbert De Gersem , Dimitrios Loukrezis