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This paper studies dynamic mechanism design in a quasilinear Markovian environment and analyzes a direct mechanism model of a principal-agent framework in which the agent is allowed to exit at any period. We consider that the agent's…

Dynamical Systems · Mathematics 2021-05-10 Tao Zhang , Quanyan Zhu

We consider the classical question of predicting binary sequences and study the {\em optimal} algorithms for obtaining the best possible regret and payoff functions for this problem. The question turns out to be also equivalent to the…

Machine Learning · Computer Science 2013-05-08 Alexandr Andoni , Rina Panigrahy

We study global optimization of non-convex functions through optimal control theory. Our main result establishes that (quasi-)optimal trajectories of a discounted control problem converge globally and practically asymptotically to the set…

Optimization and Control · Mathematics 2025-11-17 Yuyang Huang , Dante Kalise , Hicham Kouhkouh

In multi-objective optimization, a single decision vector must balance the trade-offs between many objectives. Solutions achieving an optimal trade-off are said to be Pareto optimal: these are decision vectors for which improving any one…

Optimization and Control · Mathematics 2023-08-07 Abhishek Roy , Geelon So , Yi-An Ma

This paper considers the portfolio management problem of optimal investment, consumption and life insurance. We are concerned with time inconsistency of optimal strategies. Natural assumptions, like different discount rates for consumption…

Optimization and Control · Mathematics 2011-07-25 Ivar Ekeland , Oumar Mbodji , Traian A. Pirvu

This paper studies optimal Public Private Partnerships contract between a public entity and a consortium, in continuous-time and with a continuous payment, with the possibility for the public to stop the contract. The public ("she") pays a…

Probability · Mathematics 2022-10-28 Ishak Hajjej , Caroline Hillairet , Mohamed Mnif

We consider a class of discretionary stopping problems within the $G$-framework. We first establish the well-definedness of the stopping problem under the $G$-expectation, by showing the quasi-continuity of the stopped process. We then…

Probability · Mathematics 2013-05-10 Xin Guo , Chen Pan , Shige Peng

We develop methods to solve general optimal stopping problems with opportunities to stop that arrive randomly. Such problems occur naturally in applications with market frictions. Pivotal to our approach is that our methods operate on…

Fast pricing of American-style options has been a difficult problem since it was first introduced to financial markets in 1970s, especially when the underlying stocks' prices follow some jump-diffusion processes. In this paper, we propose a…

Computational Finance · Quantitative Finance 2013-05-21 Helin Zhu , Fan Ye , Enlu Zhou

We consider a new family of derivatives whose payoffs become strictly positive when the price of their underlying asset falls relative to its historical maximum. We derive the solution to the discretionary stopping problems arising in the…

Probability · Mathematics 2016-09-26 Neofytos Rodosthenous , Mihail Zervos

This paper is concerned with the maximum principle of stochastic optimal control problems, where the coefficients of the state equation and the cost functional are uncertain, and the system is generally under Markovian regime switching.…

Optimization and Control · Mathematics 2025-04-15 Tao Hao , Jiaqiang Wen , Jie Xiong

Optimal execution of a portfolio have been a challenging problem for institutional investors. Traders face the trade-off between average trading price and uncertainty, and traditional methods suffer from the curse of dimensionality. Here,…

Portfolio Management · Quantitative Finance 2023-06-16 Xiaoyue Li , John M. Mulvey

A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value iteration (VI). Due to its good practical performance, this approximative approach is typically preferred over exact techniques, even though…

Artificial Intelligence · Computer Science 2023-04-21 Jan Křetínský , Tobias Meggendorfer , Maximilian Weininger

It is well known that mean-variance portfolio selection is a time-inconsistent optimal control problem in the sense that it does not satisfy Bellman's optimality principle and therefore the usual dynamic programming approach fails. We…

Portfolio Management · Quantitative Finance 2012-05-23 Christoph Czichowsky

This paper explores optimal insurance solutions based on the Lambda-Value-at-Risk ($\Lambda\VaR$). If the expected value premium principle is used, our findings confirm that, similar to the VaR model, a truncated stop-loss indemnity is…

Risk Management · Quantitative Finance 2025-08-19 Tim J. Boonen , Yuyu Chen , Xia Han , Qiuqi Wang

We derive closed-form solutions to the optimal stopping problems related to the pricing of perpetual American standard and lookback put and call options in the extensions of the Black-Merton-Scholes model with progressively enlarged…

Mathematical Finance · Quantitative Finance 2025-07-08 Pavel V. Gapeev , Libo Li

Rough stochastic volatility models have attracted a lot of attentions recently, in particular for the linear option pricing problem. In this paper, starting with power utilities, we propose to use a martingale distortion representation of…

Mathematical Finance · Quantitative Finance 2017-12-12 Jean-Pierre Fouque , Ruimeng Hu

In this paper, we consider discrete-time infinite horizon problems of optimal control to a terminal set of states. These are the problems that are often taken as the starting point for adaptive dynamic programming. Under very general…

Systems and Control · Computer Science 2015-10-05 Dimitri P. Bertsekas

We consider the control problem with \textit{exit time}. Unlike the Bolza and Mayer problems, in this problem the terminal time of the trajectories is not fixed, but it is the first time at which they reach a given closed subset -…

Optimization and Control · Mathematics 2017-05-10 Luong V. Nguyen

Real-world tasks involve nuanced combinations of goal and safety specifications. In high dimensions, the challenge is exacerbated: formal automata become cumbersome, and the combination of sparse rewards tends to require laborious tuning.…

Robotics · Computer Science 2026-05-15 William Sharpless , Oswin So , Dylan Hirsch , Sylvia Herbert , Chuchu Fan