標(biāo)題: Titlebook: Bilevel Optimization; Advances and Next Ch Stephan Dempe,Alain Zemkoho Book 2020 Springer Nature Switzerland AG 2020 Algorithms for linear [打印本頁(yè)] 作者: stripper 時(shí)間: 2025-3-21 19:47
書(shū)目名稱Bilevel Optimization影響因子(影響力)
作者: Fantasy 時(shí)間: 2025-3-22 00:05 作者: Immunization 時(shí)間: 2025-3-22 01:09 作者: temperate 時(shí)間: 2025-3-22 07:05 作者: GRAZE 時(shí)間: 2025-3-22 09:13
Book 2020 as a point of departure for students and researchers beginning their research journey or pursuing related projects. It also provides a unique opportunity for experienced researchers in the field to learn about the progress made so far and directions that warrant further investigation. All chapters 作者: 單純 時(shí)間: 2025-3-22 14:15
Global Search for Bilevel Optimization with Quadratic Data作者: arabesque 時(shí)間: 2025-3-22 20:48
1931-6828 pursuing related projects. It also provides a unique opportunity for experienced researchers in the field to learn about the progress made so far and directions that warrant further investigation. All chapters 978-3-030-52121-9978-3-030-52119-6Series ISSN 1931-6828 Series E-ISSN 1931-6836 作者: 過(guò)分自信 時(shí)間: 2025-3-22 22:57 作者: Kidnap 時(shí)間: 2025-3-23 02:34
On Stackelberg–Nash Equilibria in Bilevel Optimization Gamesn problems. In this paper, we survey certain properties of multiple leader–follower noncooperative games, which enable the basic Stackelberg duopoly game to encompass a larger number of decision makers at each level. We focus notably on the existence, uniqueness and welfare properties of these multi作者: 傾聽(tīng) 時(shí)間: 2025-3-23 05:56 作者: hedonic 時(shí)間: 2025-3-23 10:18
Regularization and Approximation Methods in Stackelberg Games and Bilevel Optimization different types of mathematical problems. We present formulations and solution concepts for such problems, together with their possible roles in bilevel optimization, and we illustrate the crucial issues concerning these solution concepts. Then, we discuss which of these issues can be positively or作者: daredevil 時(shí)間: 2025-3-23 15:16
Applications of Bilevel Optimization in Energy and Electricity Markets centralized planners and has become the responsibility of many different entities such as market operators, private generation companies, transmission system operators and many more. The interaction and sequence in which these entities make decisions in liberalized market frameworks have led to a r作者: 針葉 時(shí)間: 2025-3-23 21:04
Bilevel Optimization of Regularization Hyperparameters in Machine Learning Needless to say, prediction performance of ML models significantly relies on the choice of hyperparameters. Hence, establishing methodology for properly tuning hyperparameters has been recognized as one of the most crucial matters in ML. In this chapter, we introduce the role of bilevel optimizatio作者: Myocarditis 時(shí)間: 2025-3-24 00:33
Bilevel Optimization and Variational Analysis bilevel optimization with Lipschitzian data. We mainly concentrate on optimistic models, although the developed machinery also applies to pessimistic versions. Some open problems are posed and discussed.作者: 來(lái)這真柔軟 時(shí)間: 2025-3-24 02:27
Constraint Qualifications and Optimality Conditions in Bilevel Optimizationqualifications in terms of problem data and applicable optimality conditions. For the bilevel program with convex lower level program we discuss drawbacks of reformulating a bilevel programming problem by the mathematical program with complementarity constraints and present a new sharp necessary opt作者: scrutiny 時(shí)間: 2025-3-24 09:10 作者: 憤怒事實(shí) 時(shí)間: 2025-3-24 14:15 作者: 同來(lái)核對(duì) 時(shí)間: 2025-3-24 16:38
MPEC Methods for Bilevel Optimization Problemssfies a constraint qualification for all possible upper-level decisions. Replacing the lower-level optimization problem by its first-order conditions results in a mathematical program with equilibrium constraints (MPEC) that needs to be solved. We review the relationship between the MPEC and bilevel作者: Lumbar-Spine 時(shí)間: 2025-3-24 19:56 作者: Canary 時(shí)間: 2025-3-24 23:40 作者: Palpable 時(shí)間: 2025-3-25 05:31 作者: adequate-intake 時(shí)間: 2025-3-25 10:28
Bilevel Optimal Control: Existence Results and Stationarity Conditionse of them has to solve an optimal control problem of ordinary or partial differential equations. Such models are referred to as bilevel optimal control problems. Here, we first review some different features of bilevel optimal control including important applications, existence results, solution app作者: Noctambulant 時(shí)間: 2025-3-25 14:02
https://doi.org/10.1007/978-3-030-05216-4 hierarchical interactions. Nevertheless it is only recently that theoretical and numerical developments for Multi-Leader-Follower problems have been made. This chapter aims to propose a state of the art of this field of research at the frontier between optimization and economics.作者: 葡萄糖 時(shí)間: 2025-3-25 18:23
https://doi.org/10.1007/978-94-007-4954-2imality condition for the reformulation by the mathematical program with a generalized equation constraint. For the bilevel program with a nonconvex lower level program we propose a relaxed constant positive linear dependence (RCPLD) condition for the combined program.作者: 無(wú)可非議 時(shí)間: 2025-3-25 22:50
Alvar D. Gossert,Wolfgang Jahnkelevel optimization problem. Most of them are exact algorithms, with only a few applying metaheuristic techniques. In this chapter, both kind of algorithms are reviewed according to the underlying idea that justifies them.作者: Semblance 時(shí)間: 2025-3-26 01:12
Fritz Schultz-Grunow,Herbert Zeibigly, we give reference to numerical approaches which have been followed in the literature to solve these kind of problems. We concentrate in this chapter on nonlinear problems, while the results and statements naturally also hold for the linear case.作者: chalice 時(shí)間: 2025-3-26 06:50 作者: 有害 時(shí)間: 2025-3-26 08:42 作者: NATAL 時(shí)間: 2025-3-26 13:35
1931-6828 emerging applications, particularly in data analytics, secur.2019 marked the 85th anniversary of Heinrich Freiherr von Stackelberg’s habilitation thesis “Marktform und Gleichgewicht,” which formed the roots of bilevel optimization. Research on the topic has grown tremendously since its introduction 作者: Cosmopolitan 時(shí)間: 2025-3-26 17:55 作者: CHARM 時(shí)間: 2025-3-27 00:33
Isotope Shifts in X-Ray Spectra,rly tuning hyperparameters has been recognized as one of the most crucial matters in ML. In this chapter, we introduce the role of bilevel optimization in the context of selecting hyperparameters in regression and classification problems.作者: 子女 時(shí)間: 2025-3-27 04:05
Isotope Labeling in Insect Cellsthe standard methods of convex optimization. Hence several algorithms have been developed in the literature to tackle this problem. In this article we discuss several such algorithms including recent ones.作者: Neutral-Spine 時(shí)間: 2025-3-27 09:08
https://doi.org/10.1007/978-94-007-4954-2results in a mathematical program with equilibrium constraints (MPEC) that needs to be solved. We review the relationship between the MPEC and bilevel optimization problem and then survey the theory, algorithms, and software environments for solving the MPEC formulations.作者: 施舍 時(shí)間: 2025-3-27 13:19
Isotope-Based Quantum Informations and properties to solution approaches. It will directly support researchers in understanding theoretical research results, designing solution algorithms in relation to pessimistic bilevel optimization.作者: 滲透 時(shí)間: 2025-3-27 15:48
On Stackelberg–Nash Equilibria in Bilevel Optimization Gamesame to encompass a larger number of decision makers at each level. We focus notably on the existence, uniqueness and welfare properties of these multiple leader–follower games. We also study how this particular bilevel optimization game can be extended to a multi-level decision setting.作者: 澄清 時(shí)間: 2025-3-27 18:47
Bilevel Optimization of Regularization Hyperparameters in Machine Learningrly tuning hyperparameters has been recognized as one of the most crucial matters in ML. In this chapter, we introduce the role of bilevel optimization in the context of selecting hyperparameters in regression and classification problems.作者: 軍火 時(shí)間: 2025-3-28 00:30
Algorithms for Simple Bilevel Programmingthe standard methods of convex optimization. Hence several algorithms have been developed in the literature to tackle this problem. In this article we discuss several such algorithms including recent ones.作者: 協(xié)迫 時(shí)間: 2025-3-28 05:02 作者: 入會(huì) 時(shí)間: 2025-3-28 10:03 作者: esoteric 時(shí)間: 2025-3-28 12:42
https://doi.org/10.1007/978-3-030-63010-2rent Nash-like models that are related to the (approximated) pessimistic version of the bilevel problem. This analysis, being of independent theoretical interest, leads also to algorithmic developments. Finally, we discuss the intrinsic complexity characterizing both the optimistic bilevel and the Nash game models.作者: harbinger 時(shí)間: 2025-3-28 14:59 作者: N防腐劑 時(shí)間: 2025-3-28 22:33
Fritz Schultz-Grunow,Herbert Zeibigmal control problem of partial differential equations have to be reconstructed. After verifying the existence of solutions, necessary optimality conditions are derived by exploiting the optimal value function of the underlying parametric optimal control problem in the context of a relaxation approach.作者: Flat-Feet 時(shí)間: 2025-3-29 00:02 作者: 驚惶 時(shí)間: 2025-3-29 05:01 作者: Palpitation 時(shí)間: 2025-3-29 07:37 作者: Mosaic 時(shí)間: 2025-3-29 14:01 作者: 煩擾 時(shí)間: 2025-3-29 18:53
Constraint Qualifications and Optimality Conditions in Bilevel Optimizationimality condition for the reformulation by the mathematical program with a generalized equation constraint. For the bilevel program with a nonconvex lower level program we propose a relaxed constant positive linear dependence (RCPLD) condition for the combined program.作者: 有特色 時(shí)間: 2025-3-29 21:17
Algorithms for Linear Bilevel Optimizationlevel optimization problem. Most of them are exact algorithms, with only a few applying metaheuristic techniques. In this chapter, both kind of algorithms are reviewed according to the underlying idea that justifies them.作者: 搜尋 時(shí)間: 2025-3-30 01:33 作者: pellagra 時(shí)間: 2025-3-30 04:54 作者: Ballerina 時(shí)間: 2025-3-30 11:13 作者: 走路左晃右晃 時(shí)間: 2025-3-30 12:39 作者: 糾纏,纏繞 時(shí)間: 2025-3-30 17:25 作者: BUOY 時(shí)間: 2025-3-30 21:47
Isotope Low-Dimensional Structures different types of mathematical problems. We present formulations and solution concepts for such problems, together with their possible roles in bilevel optimization, and we illustrate the crucial issues concerning these solution concepts. Then, we discuss which of these issues can be positively or作者: 牲畜欄 時(shí)間: 2025-3-31 04:21 作者: CLOT 時(shí)間: 2025-3-31 07:11 作者: FILLY 時(shí)間: 2025-3-31 12:41
Isotope Enhanced Approaches in Metabolomics bilevel optimization with Lipschitzian data. We mainly concentrate on optimistic models, although the developed machinery also applies to pessimistic versions. Some open problems are posed and discussed.作者: COST 時(shí)間: 2025-3-31 15:59
https://doi.org/10.1007/978-94-007-4954-2qualifications in terms of problem data and applicable optimality conditions. For the bilevel program with convex lower level program we discuss drawbacks of reformulating a bilevel programming problem by the mathematical program with complementarity constraints and present a new sharp necessary opt作者: choroid 時(shí)間: 2025-3-31 19:04 作者: PALL 時(shí)間: 2025-3-31 22:19 作者: 明確 時(shí)間: 2025-4-1 05:43
https://doi.org/10.1007/978-94-007-4954-2sfies a constraint qualification for all possible upper-level decisions. Replacing the lower-level optimization problem by its first-order conditions results in a mathematical program with equilibrium constraints (MPEC) that needs to be solved. We review the relationship between the MPEC and bilevel作者: HPA533 時(shí)間: 2025-4-1 07:07
Isotope-Based Quantum Informationnging search and optimization problems. In this chapter, we discuss recent population-based evolutionary algorithms for solving different types of bilevel optimization problems, as they pose numerous challenges to an optimization algorithm. Evolutionary bilevel optimization (EBO) algorithms are gain
