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In this paper we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users/buyers. It needs to determine how to optimally price the…

Computational Engineering, Finance, and Science · Computer Science 2012-07-30 Shang-Pin Sheng , Mingyan Liu

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision…

Machine Learning · Computer Science 2023-05-11 Tobias Enders , James Harrison , Marco Pavone , Maximilian Schiffer

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of…

Machine Learning · Statistics 2020-09-23 Brian Sifringer , Virginie Lurkin , Alexandre Alahi

We consider the terminal wealth utility maximization problem from the point of view of a portfolio manager who is paid by an incentive scheme, which is given as a convex function $g$ of the terminal wealth. The manager's own utility…

Portfolio Management · Quantitative Finance 2015-02-24 Maxim Bichuch , Stephan Sturm

Electricity market operators worldwide use mixed-integer linear programming to solve the allocation problem in wholesale electricity markets. Prices are typically determined based on the duals of relaxed versions of this optimization…

Computer Science and Game Theory · Computer Science 2023-12-13 Mete Şeref Ahunbay , Martin Bichler , Teodora Dobos , Johannes Knörr

In this brief, we consider the constrained optimization problem underpinning model predictive control (MPC). We show that this problem can be decomposed into an unconstrained optimization problem with the same cost function as the original…

Optimization and Control · Mathematics 2020-08-18 Uroš Kalabić , Ilya Kolmanovsky

In this paper, we consider a multi-stage dynamic assortment optimization problem with multi-nomial choice modeling (MNL) under resource knapsack constraints. Given the current resource inventory levels, the retailer makes an assortment…

Optimization and Control · Mathematics 2025-11-05 Xi Chen , Mo Liu , Yining Wang , Yuan Zhou

Many information systems use tags and keywords to describe and annotate content. These allow for efficient organization and categorization of items, as well as facilitate relevant search queries. As such, the selected set of tags for an…

Social and Information Networks · Computer Science 2016-05-20 Nir Rosenfeld , Amir Globerson

We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of…

Machine Learning · Statistics 2017-01-06 Max Yi Ren , Clayton Scott

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates…

Optimization and Control · Mathematics 2017-06-13 Teobaldo Bulhões , Minh Hoàng Hà , Rafael Martinelli , Thibaut Vidal

We consider dynamic pricing with many products under an evolving but low-dimensional demand model. Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show…

Machine Learning · Computer Science 2019-09-12 Jonas Mueller , Vasilis Syrgkanis , Matt Taddy

Correctly estimating how demand respond to prices is fundamental for airlines willing to optimize their pricing policy. Under some conditions, these policies, while aiming at maximizing short term revenue, can present too little price…

Machine Learning · Computer Science 2022-03-22 Giovanni Gatti Pinheiro , Michael Defoin-Platel , Jean-Charles Regin

Implicit variables of an optimization problem are used to model variationally challenging feasibility conditions in a tractable way while not entering the objective function. Hence, it is a standard approach to treat implicit variables as…

Optimization and Control · Mathematics 2025-10-01 Patrick Mehlitz

The rapid advancement in large language models (LLMs) has brought forth a diverse range of models with varying capabilities that excel in different tasks and domains. However, selecting the optimal LLM for user queries often involves a…

Machine Learning · Computer Science 2025-02-06 Yang Li

A competing market model with a polyvariant profit function that assumes "zeitnot" stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An…

Portfolio Management · Quantitative Finance 2010-05-18 Bohdan Yu. Kyshakevych , Anatoliy K. Prykarpatsky , Denis Blackmore , Ivan P. Tverdokhlib

Constrained decision-making is essential for designing safe policies in real-world control systems, yet simulated environments often fail to capture real-world adversities. We consider the problem of learning a policy that will maximize the…

Machine Learning · Computer Science 2026-02-10 Sourav Ganguly , Kishan Panaganti , Arnob Ghosh , Adam Wierman

In this paper, we investigate the capacitated assortment optimization problem with pricing under the paired combinatorial logit model, whose goal is to identify the revenue-maximizing subset of products as well as their selling prices…

Optimization and Control · Mathematics 2018-10-24 Daihan Zhang , Zhenghe Zhong , Chuning Gao , Rui Chen

This paper studies an $\alpha$-robust utility maximization problem where an investor faces an intractable claim -- an exogenous contingent claim with known marginal distribution but unspecified dependence structure with financial market…

Portfolio Management · Quantitative Finance 2026-04-07 Xinyu Chen , Zuo Quan Xu

We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model with static sampling. The goal is to decide whether the value of a given policy exceeds a specified…

Machine Learning · Statistics 2026-04-21 Kaito Ariu , Po-An Wang , Alexandre Proutiere , Kenshi Abe

We consider a principal who wishes to screen an agent with \emph{discrete} types by offering a menu of \emph{discrete} quantities and \emph{discrete} transfers. We assume that the principal's valuation is discrete strictly concave and use a…

Theoretical Economics · Economics 2025-10-27 Alejandro Francetich , Burkhard C. Schipper
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