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Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for multilinear data analysis against outliers, gross corruptions and missing values and has a diverse array of applications. In this paper, we study…

Machine Learning · Statistics 2014-08-26 Donald Goldfarb , Zhiwei Qin

This article develops a control method for linear time-invariant systems subject to time-varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we…

Systems and Control · Electrical Eng. & Systems 2026-02-02 Marko Nonhoff , Mohammad Taher Al Torshan , Matthias A. Müller

In many applications, such as economics, operations research and reinforcement learning, one often needs to estimate a multivariate regression function f subject to a convexity constraint. For example, in sequential decision processes the…

Methodology · Statistics 2011-09-05 Lauren A. Hannah , David B. Dunson

This paper studies a type of periodic utility maximization problems for portfolio management in incomplete stochastic factor models with convex trading constraints. The portfolio performance is periodically evaluated on the relative ratio…

Mathematical Finance · Quantitative Finance 2024-11-22 Wenyuan Wang , Kaixin Yan , Xiang Yu

Consider the problem of minimizing the expected value of a (possibly nonconvex) cost function parameterized by a random (vector) variable, when the expectation cannot be computed accurately (e.g., because the statistics of the random…

Multiagent Systems · Computer Science 2017-12-12 Yang Yang , Gesualdo Scutari , Daniel P. Palomar , Marius Pesavento

A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic inference on a graphical model of the Markov decision process (MDP). The core object of study is the probability of each state-action pair being visited…

Machine Learning · Computer Science 2023-11-23 Jean Tarbouriech , Tor Lattimore , Brendan O'Donoghue

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

Traditional solvable optimal control theory predominantly focuses on quadratic costs due to their analytical tractability, yet they often fail to capture critical non-linearities inherent in real-world systems including water, energy,…

Optimization and Control · Mathematics 2025-05-22 Julian Barreiro-Gomez , Tyrone E. Duncan , Bozenna Pasik-Duncan , Hamidou Tembine

We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns…

Machine Learning · Computer Science 2017-07-05 Robert Chen , Brendan Lucier , Yaron Singer , Vasilis Syrgkanis

Optimization problems with convex quadratic cost and polyhedral constraints are ubiquitous in signal processing, automatic control and decision-making. We consider here an enlarged problem class that allows to encode logical conditions and…

Optimization and Control · Mathematics 2026-04-09 Alberto De Marchi

Convex optimization problems with staged structure appear in several contexts, including optimal control, verification of deep neural networks, and isotonic regression. Off-the-shelf solvers can solve these problems but may scale poorly. We…

Optimization and Control · Mathematics 2020-10-28 Rudy Bunel , Oliver Hinder , Srinadh Bhojanapalli , Krishnamurthy , Dvijotham

Motivated by the grid search method and Bayesian optimization, we introduce the concept of contractibility and its applications in model-based optimization. First, a basic framework of contraction methods is established to construct a…

Optimization and Control · Mathematics 2021-08-24 Xiaopeng Luo , Xin Xu

This paper proposes and analyzes a communication-efficient distributed optimization framework for general nonconvex nonsmooth signal processing and machine learning problems under an asynchronous protocol. At each iteration, worker machines…

Optimization and Control · Mathematics 2020-07-15 Jineng Ren , Jarvis Haupt

We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization problem relaxed from an exact mixed integer non-linear trajectory…

Robotics · Computer Science 2025-03-19 Patrizio Perugini , Jens Lundell , Katharina Friedl , Danica Kragic

We consider a stochastic control problem where the set of strict (classical) controls is not necessarily convex, and the system is governed by a nonlinear backward stochastic differential equation. By introducing a new approach, we…

Optimization and Control · Mathematics 2008-12-20 Seid Bahlali

We employ a fuzzy optimality condition for the Frechet subdifferential and some advanced techniques of variational analysis such as formulae for the subdifferentials of an infinite family of nonsmooth functions and the coderivative…

Optimization and Control · Mathematics 2022-11-16 Maryam Saadati , Morteza Oveisiha

In that paper, we provide a new characterization of the solutions of specific reflected backward stochastic differential equations (or RBSDEs) whose driver $g$ is convex and has quadratic growth in its second variable: this is done by…

Pricing of Securities · Quantitative Finance 2008-12-02 Marie-Amelie Morlais

Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursive feasibility. In this work, we present a Probabilistic…

Systems and Control · Electrical Eng. & Systems 2026-05-20 Hyeontae Sung , Hyeongchan Ham , Junyoung Park , Kai Ren , Heejin Ahn

Inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. This problem corresponds to the minimization of the sum of a (weakly) smooth function endowed…

Machine Learning · Computer Science 2024-01-24 Alexandre d'Aspremont , Cristóbal Guzmán , Clément Lezane

This work investigates the challenge of ensuring safety guarantees in the presence of uncontrollable agents, whose behaviors are stochastic and depend on both their own and the system's states. We present a neural model predictive control…

Systems and Control · Electrical Eng. & Systems 2026-04-21 Shuqi Wang , Mingyang Feng , Yu Chen , Yue Gao , Xiang Yin