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Recently, literature on dynamic coherent risk measures has broadened the choices for risk-sensitive performance evaluation. A running example includes Cumulative prospect theory and Conditional variance at risk. Most of them can be can be…

Optimization and Control · Mathematics 2020-12-14 Weixin Wang

We consider a real options model for the optimal irreversible investment problem of a profit maximizing company. The company has the opportunity to invest into a production plant capable of producing two products, of which the prices follow…

Mathematical Finance · Quantitative Finance 2021-07-09 Felix Dammann , Giorgio Ferrari

In this work, we consider constrained stochastic optimization problems under hidden convexity, i.e., those that admit a convex reformulation via non-linear (but invertible) map $c(\cdot)$. A number of non-convex problems ranging from…

Optimization and Control · Mathematics 2024-11-12 Ilyas Fatkhullin , Niao He , Yifan Hu

We consider the problem of choosing an optimal portfolio, assuming the asset returns have a Gaussian mixture (GM) distribution, with the objective of maximizing expected exponential utility. In this paper we show that this problem is…

Optimization and Control · Mathematics 2022-08-12 Eric Luxenberg , Stephen Boyd

In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP,…

Machine Learning · Computer Science 2024-01-08 Nirjhar Das , Arpan Chattopadhyay

This paper considers an insurance company that faces two key constraints: a ratcheting dividend constraint and an irreversible reinsurance constraint. The company allocates part of its reserve to pay dividends to its shareholders while…

Optimization and Control · Mathematics 2025-12-22 Tim J. Boonen , Engel John C. Dela Vega

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task robustly: i.e., we…

Optimization and Control · Mathematics 2019-06-05 Jack Umenberger , Mina Ferizbegovic , Thomas B. Schön , Håkan Hjalmarsson

We consider the mixed regression problem with two components, under adversarial and stochastic noise. We give a convex optimization formulation that provably recovers the true solution, and provide upper bounds on the recovery errors for…

Machine Learning · Statistics 2015-02-16 Yudong Chen , Xinyang Yi , Constantine Caramanis

Inverse optimization, determining parameters of an optimization problem that render a given solution optimal, has received increasing attention in recent years. While significant inverse optimization literature exists for convex…

Optimization and Control · Mathematics 2021-09-02 Merve Bodur , Timothy C. Y. Chan , Ian Yihang Zhu

In this paper, we propose a new policy iteration algorithm to compute the value function and the optimal controls of continuous time stochastic control problems. The algorithm relies on successive approximations using linear-quadratic…

Optimization and Control · Mathematics 2024-09-09 Dylan Possamaï , Ludovic Tangpi

Periodic operation often emerges as the economically optimal mode in industrial processes, particularly under varying economic or environmental conditions. This paper proposes a robust model predictive control (MPC) framework for uncertain…

Systems and Control · Electrical Eng. & Systems 2025-12-23 Filippo Badalamenti , Jose A. Borja-Conde , Sampath Kumar Mulagaleti , Boris Houska , Alberto Bemporad , Mario Eduardo Villanueva

We present an efficient framework for solving algebraically-constrained global non-convex polynomial optimization problems over subsets of the hypercube. We prove the existence of an equivalent nonlinear reformulation of such problems that…

Optimization and Control · Mathematics 2024-09-05 Mitchell Tong Harris , Pierre-David Letourneau , Dalton Jones , M. Harper Langston

In this paper we consider three minimization problems, namely quadratic, $\rho$-convex and quadratic fractional programing problems. The quadratic problem is considered with quadratic inequality constraints with bounded continuous and…

Optimization and Control · Mathematics 2018-04-09 B. Muraleetharan , S. Selvarajan , S. Srisatkunarajah , K. Thirulogasanthar

In this paper, we study a nonsmooth/nonconvex multiobjective optimization problem with uncertain constraints in arbitrary Asplund spaces. We first provide necessary optimality condition in a fuzzy form for approximate weakly robust…

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

This paper studies a robust utility maximization problem for intractable claims under distributional ambiguity, where the distribution of the claim cannot be inferred from market information and its dependence with tradable assets is…

Optimization and Control · Mathematics 2026-04-17 Guohui Guan , Zongxia Liang , Xingjian Ma

Stochastic maximum principle of nonlinear controlled forward-backward systems, where the set of strict (classical) controls need not be convex and the diffusion coefficient depends explicitly on the variable control, is an open problem…

Probability · Mathematics 2008-12-20 Seid Bahlali

We study non-convex Hamilton-Jacobi equations in the presence of gradient constraints and produce new, optimal, regularity results for the solutions. A distinctive feature of those equations regards the existence of a lower bound to the…

Analysis of PDEs · Mathematics 2020-10-27 Héctor A. Chang-Lara , Edgard A. Pimentel

In this paper, we consider an adaptive approach to address optimization problems with uncertain cost parameters. Here, the decision maker selects an initial decision, observes the realization of the uncertain cost parameters, and then is…

Computational Complexity · Computer Science 2013-12-17 Ebrahim Nasrabadi , James B. Orlin

We study learning problems involving arbitrary classes of functions $F$, distributions $X$ and targets $Y$. Because proper learning procedures, i.e., procedures that are only allowed to select functions in $F$, tend to perform poorly unless…

Machine Learning · Statistics 2018-04-17 Shahar Mendelson

This paper studies distributionally robust optimization for a rich class of risk measures with ambiguity sets defined by $\phi$-divergences. The risk measures are allowed to be non-linear in probabilities, are represented by Choquet…

Optimization and Control · Mathematics 2025-04-15 Guanyu Jin , Roger J. A. Laeven , Dick den Hertog
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