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In bilevel optimization problems, a leader and a follower make their decisions in a hierarchy, and both decisions may influence each other. Usually one assumes that both players have full knowledge also of the other player's data. In a more…

最优化与控制 · 数学 2026-02-19 Dorothee Henke

Bilevel programming is one of the very active areas of research with many real-life applications in economics and engineering. Bilevel problems are hierarchical problems consisting of lower-level and upper-level problems, respectively. The…

最优化与控制 · 数学 2025-10-24 Kuntal Som , Thirumulanathan D , Joydeep Dutta

In this study, we consider a class of linear matroid interdiction problems, where the feasible sets for the upper-level decision-maker (referred to as a leader) and the lower-level decision-maker (referred to as a follower) are induced by…

计算复杂性 · 计算机科学 2025-08-26 Sergey S. Ketkov , Oleg A. Prokopyev

This paper presents a comprehensive review of techniques proposed in the literature for solving bilevel optimization problems encountered in various real-life applications. Bilevel optimization is an appropriate choice for hierarchical…

最优化与控制 · 数学 2025-11-06 Dhaval Pujara , Ankur Sinha

We investigate the complexity of bilevel combinatorial optimization with uncertainty in the follower's objective, in a robust optimization approach. We show that the robust counterpart of the bilevel problem under interval uncertainty can…

最优化与控制 · 数学 2021-08-05 Christoph Buchheim , Dorothee Henke , Felix Hommelsheim

We consider a bilevel continuous knapsack problem where the leader controls the capacity of the knapsack, while the follower chooses a feasible packing maximizing his own profit. The leader's aim is to optimize a linear objective function…

数据结构与算法 · 计算机科学 2022-07-19 Christoph Buchheim , Dorothee Henke , Jannik Irmai

Bilevel linear programming (LP) is one of the simplest classes of bilevel optimization problems, yet it is known to be NP-hard in general. Specifically, determining whether the optimal objective value of a bilevel LP is at least as good as…

最优化与控制 · 数学 2026-03-23 Nagisa Sugishita , Margarida Carvalho

Bilevel linear programs (BLPs) form a class of hierarchical decision-making problems in which both the upper-level and the lower-level decision-makers, known as the leader and the follower, respectively, solve linear optimization problems.…

计算复杂性 · 计算机科学 2025-11-20 Sergey S. Ketkov , Oleg A. Prokopyev

We consider bilevel linear problems, where the right-hand side of the lower level problems is stochastic. The leader has to decide in a here-and-now fashion, while the follower has complete information. In this setting, the leader's outcome…

最优化与控制 · 数学 2019-07-11 Johanna Burtscheidt , Matthias Claus

We consider a bilevel continuous knapsack problem where the leader controls the capacity of the knapsack and the follower chooses an optimal packing according to his own profits, which may differ from those of the leader. To this bilevel…

数据结构与算法 · 计算机科学 2022-07-19 Christoph Buchheim , Dorothee Henke

Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reaction. Constrained bilevel problems with integer variables…

最优化与控制 · 数学 2024-11-04 Justin Dumouchelle , Esther Julien , Jannis Kurtz , Elias B. Khalil

We consider discrete bilevel optimization problems where the follower solves an integer program with a fixed number of variables. Using recent results in parametric integer programming, we present polynomial time algorithms for pure and…

最优化与控制 · 数学 2017-01-03 Matthias Köppe , Maurice Queyranne , Christopher Thomas Ryan

Both bilevel and robust optimization are established fields of mathematical optimization and operations research. However, only until recently, the similarities in their mathematical structure has neither been studied theoretically nor…

最优化与控制 · 数学 2026-02-20 Henri Lefebvre , Martin Schmidt , Simon Stevens , Johannes Thürauf

Bilevel learning refers to machine learning problems that can be formulated as bilevel optimization models, where decisions are organized in a hierarchical structure. This paradigm has recently gained considerable attention in machine…

最优化与控制 · 数学 2026-05-05 Riccardo Grazzi , Massimiliano Pontil , Saverio Salzo , Alain Zemkoho

Bilevel programming problems frequently arise in real-world applications across various fields, including transportation, economics, energy markets and healthcare. These problems have been proven to be NP-hard even in the simplest form with…

最优化与控制 · 数学 2024-09-06 Sina Hajikazemi , Florian Steinke

Bilevel optimization formulates hierarchical decision-making processes that arise in many real-world applications such as in pricing, network design, and infrastructure defense planning. In this paper, we consider a class of bilevel…

最优化与控制 · 数学 2021-04-20 Geunyeong Byeon , Pascal Van Hentenryck

We design and analyze a novel accelerated gradient-based algorithm for a class of bilevel optimization problems. These problems have various applications arising from machine learning and image processing, where optimal solutions of the two…

最优化与控制 · 数学 2023-11-20 Sepideh Samadi , Daniel Burbano , Farzad Yousefian

We introduce a new bilevel version of the classic shortest path problem and completely characterize its computational complexity with respect to several problem variants. In our problem, the leader and the follower each control a subset of…

数据结构与算法 · 计算机科学 2026-02-19 Dorothee Henke , Lasse Wulf

In this letter, we consider a bilevel optimization problem in which the outer-level objective function is strongly convex, whereas the inner-level problem consists of a finite sum of convex functions. Bilevel optimization problems arise in…

最优化与控制 · 数学 2026-01-22 Sudkobfa Boontawee , Mootta Prangprakhon , Nimit Nimana

Bilevel Optimization Programming is used to model complex and conflicting interactions between agents, for example in Robust AI or Privacy-preserving AI. Integrating bilevel mathematical programming within deep learning is thus an essential…

机器学习 · 计算机科学 2023-03-01 Francesco Alesiani
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