標題: Titlebook: Numerical Nonsmooth Optimization; State of the Art Alg Adil M. Bagirov,Manlio Gaudioso,Sona Taheri Book 2020 Springer Nature Switzerland AG [打印本頁] 作者: Ensign 時間: 2025-3-21 19:49
書目名稱Numerical Nonsmooth Optimization影響因子(影響力)
作者: 兇殘 時間: 2025-3-21 20:31 作者: 四指套 時間: 2025-3-22 02:54
Adil M. Bagirov,Manlio Gaudioso,Sona TaheriProvides a comprehensive coverage of both traditional and more advanced nonsmooth optimization methods.Gathers for the first time the founding fathers and mothers of the respective nonsmooth optimizat作者: forebear 時間: 2025-3-22 07:51
978-3-030-34912-7Springer Nature Switzerland AG 2020作者: xanthelasma 時間: 2025-3-22 11:34
Gradient Sampling Methods for Nonsmooth Optimization problems. We also clarify certain technical aspects of the analysis of gradient sampling algorithms, most notably related to the assumptions one needs to make about the set of points at which the objective is continuously differentiable. Finally, we discuss possible future research directions.作者: HUSH 時間: 2025-3-22 13:03 作者: 煩躁的女人 時間: 2025-3-22 19:39 作者: 總 時間: 2025-3-22 22:39 作者: GRAIN 時間: 2025-3-23 02:02 作者: Herbivorous 時間: 2025-3-23 06:43 作者: Palpitation 時間: 2025-3-23 11:15
Local Search for Nonsmooth DC Optimization with DC Equality and Inequality Constraintshe cluster point of the sequence is the KKT point for the original problem with the Lagrange multipliers provided by an auxiliary linearized problem. Finally, on the base of the developed theory several new stopping criteria are elaborated, which allow to transform the local search scheme into a local search algorithm.作者: 眉毛 時間: 2025-3-23 17:29
Beyond the Oracle: Opportunities of Piecewise Differentiationdescribe the calculation of directionally active generalized gradients, generalized .-gradients and the checking of first and second order optimality conditions. All this is based on the abs-linearization of a piecewise smooth objective in abs-normal form.作者: 食道 時間: 2025-3-23 20:48 作者: TEM 時間: 2025-3-23 22:57 作者: 名字 時間: 2025-3-24 03:22
Bundle Methods for Nonsmooth DC Optimizationhed. Bundle methods are developed based on a nonconvex piecewise linear model of the objective function and the convergence of these methods is studied. Numerical results are presented to demonstrate the performance of the methods.作者: Arctic 時間: 2025-3-24 10:19
On Mixed Integer Nonsmooth Optimizationby using Clarke subgradients as a substitute for the classical gradient. Ideas for convergence proofs are given as well as references where the details can be found. We also consider how some algorithms can be modified in order to solve nonconvex problems including ..-pseudoconvex functions or even ..-quasiconvex constraints.作者: ALE 時間: 2025-3-24 13:23 作者: 招募 時間: 2025-3-24 16:27
Advances in Low-Memory Subgradient Optimization to execute these algorithms. To provide historical perspective this survey starts with the original result of Shor which opened this field with the application to the classical transportation problem. The theoretical complexity bounds for smooth and nonsmooth convex and quasiconvex optimization pro作者: 窗簾等 時間: 2025-3-24 22:23
Standard Bundle Methods: Untrusted Models and Dualitypproaches are based on constructing models of the function, but lack of continuity of first-order information implies that these models cannot be trusted, not even close to an optimum. Therefore, many different forms of stabilization have been proposed to try to avoid being led to areas where the mo作者: 發(fā)現(xiàn) 時間: 2025-3-25 03:00
A Second Order Bundle Algorithm for Nonsmooth, Nonconvex Optimization Problems method by Fendl and Schichl (A feasible second order bundle algorithm for nonsmooth, nonconvex optimization problems with inequality constraints: I. derivation and convergence. arXiv:1506.07937, 2015, preprint) to the general nonlinearly constrained case. Instead of using a penalty function or a fi作者: 加劇 時間: 2025-3-25 06:34 作者: 嬉耍 時間: 2025-3-25 07:37 作者: 伸展 時間: 2025-3-25 12:10 作者: decipher 時間: 2025-3-25 16:59
Bundle Methods for Nonsmooth DC Optimizationconditions are discussed and the relationship between sets of different stationary points (critical, Clarke stationary and inf-stationary) is established. Bundle methods are developed based on a nonconvex piecewise linear model of the objective function and the convergence of these methods is studie作者: Sarcoma 時間: 2025-3-25 21:25 作者: 使成整體 時間: 2025-3-26 02:47
Beyond the Oracle: Opportunities of Piecewise Differentiationoracle that evaluates at any given . the objective function value .(.) and a generalized gradient .?∈?.(.) in the sense of Clarke. We will argue here that, if there is a realistic possibility of computing a vector . that is guaranteed to be a generalized gradient, then one must know so much about th作者: Abduct 時間: 2025-3-26 05:01
Numerical Solution of Generalized Minimax Problemssts in the minimization of nonsmooth functions which are compositions of special smooth convex functions with maxima of smooth functions. The most important functions of this type are the sums of maxima of smooth functions. Section 11.2 is devoted to primal interior point methods which use solutions作者: 抱負 時間: 2025-3-26 11:19 作者: 同謀 時間: 2025-3-26 15:12
New Multiobjective Proximal Bundle Method with Scaled Improvement Functioncase the improvement function possesses, for example the nice property that a descent direction for the improvement function improves all the objectives of the original problem. However, the numerical experiments have shown that the standard improvement function is rather sensitive for scaling. For 作者: VICT 時間: 2025-3-26 19:23 作者: 直覺沒有 時間: 2025-3-26 21:14 作者: 案發(fā)地點 時間: 2025-3-27 03:23
On Mixed Integer Nonsmooth Optimizationnd, outer approximation, extended cutting plane, extended supporting hyperplane and extended level bundle method. Nonsmoothness is taken into account by using Clarke subgradients as a substitute for the classical gradient. Ideas for convergence proofs are given as well as references where the detail作者: watertight, 時間: 2025-3-27 06:55
of design methodologies have been proposed. However, sensitive adjustment of related control parameters remains entrusted to users, because rendering conditions, such as the thickness of emphasized subvolumes in the ray direction and the size of a target dataset, differ on a case-by-case basis. Our作者: 青少年 時間: 2025-3-27 10:28
Adil M. Bagirov,Manlio Gaudioso,Napsu Karmitsa,Marko M. M?kel?,Sona Taheri of design methodologies have been proposed. However, sensitive adjustment of related control parameters remains entrusted to users, because rendering conditions, such as the thickness of emphasized subvolumes in the ray direction and the size of a target dataset, differ on a case-by-case basis. Our作者: 參考書目 時間: 2025-3-27 15:43 作者: coagulate 時間: 2025-3-27 20:51 作者: 埋葬 時間: 2025-3-27 23:49
Antonio Frangioniectory living in a high-dimensional phase space. The high dimensionality leads to visualization challenges and, for the case of inertial particles, multiple models exist that pose different assumptions. In this paper, we thoroughly address the extraction of a specific feature, namely the vortex core作者: Irritate 時間: 2025-3-28 04:03
Hermann Schichl,Hannes Fendlectory living in a high-dimensional phase space. The high dimensionality leads to visualization challenges and, for the case of inertial particles, multiple models exist that pose different assumptions. In this paper, we thoroughly address the extraction of a specific feature, namely the vortex core作者: SMART 時間: 2025-3-28 09:26
Napsu Karmitsaork has been inspired by the observation that in many applications a large variety of different feature definitions for the same concept are used. Often, these definitions compete with each other and it is unclear which definition should be used in which context. A prominent example is the definitio作者: 我沒有命令 時間: 2025-3-28 10:46
James V. Burke,Frank E. Curtis,Adrian S. Lewis,Michael L. Overton,Lucas E. A. Sim?ese of data sets. However, topological analysis is difficult to parallelize on distributed memory systems – and thus to utilize for in situ visualization – due to the global nature of topological descriptors..This chapter presents and evaluates a task-parallel formulation of topology-controlled volume作者: Host142 時間: 2025-3-28 17:12 作者: 演講 時間: 2025-3-28 18:49 作者: 入會 時間: 2025-3-28 23:16 作者: nurture 時間: 2025-3-29 04:43 作者: Contort 時間: 2025-3-29 10:43
Andreas Griewank,Andrea Waltherded for group theory.Contains more than 40 figures.Immense p.Topological Methods in Group Theory. is about the interplay between algebraic topology and the theory of infinite discrete groups. The author has kept three kinds of readers in mind: graduate students who have had an introductory course in作者: archaeology 時間: 2025-3-29 15:16
Ladislav Luk?an,Ctirad Matonoha,Jan Vl?ekded for group theory.Contains more than 40 figures.Immense p.Topological Methods in Group Theory. is about the interplay between algebraic topology and the theory of infinite discrete groups. The author has kept three kinds of readers in mind: graduate students who have had an introductory course in作者: 催眠 時間: 2025-3-29 17:39 作者: Synchronism 時間: 2025-3-29 22:25 作者: 慢慢沖刷 時間: 2025-3-30 02:57
Advances in Low-Memory Subgradient Optimizationallow to solve nonsmooth convex optimization problems faster than dictate theoretical lower complexity bounds. In this work the particular attention is given to Nesterov smoothing technique, Nesterov universal approach, and Legendre (saddle point) representation approach. The new results on universa作者: 主動 時間: 2025-3-30 07:00 作者: 侵略者 時間: 2025-3-30 11:40
Beyond First Order: ,-Decomposition Methodspace, an intermediate iterate is defined such that the overall convergence is driven by the .-step. As a result, the serious-step subsequence converges with superlinear speed..By focusing on the proximal variants of bundle methods, this chapter introduces gradually the .-theory and the ingredients n作者: 提升 時間: 2025-3-30 14:36
Bundle Methods for Inexact Datah which, although not exaustive, covers various classes of bundle methods and various types of inexact oracles, for unconstrained and convexly constrained problems (with both convex and nonconvex objective functions), as well as nonsmooth mixed-integer optimization.作者: hedonic 時間: 2025-3-30 19:48 作者: 頌揚本人 時間: 2025-3-30 23:45
Mixed-Integer Linear Optimization: Primal–Dual Relations and Dual Subgradient and Cutting-Plane Methts to solving the convexified problem by Dantzig–Wolfe decomposition, as well as a two-phase method that benefits from the advantages of both subgradient optimization and Dantzig–Wolfe decomposition. Finally, we describe how the Lagrangian dual approach can be used to find near optimal solutions to 作者: carotid-bruit 時間: 2025-3-31 01:59 作者: 多樣 時間: 2025-3-31 06:41
Antonio Frangioniertial particle models at once, we extend the concept of second-order vortex corelines to the inertial case and make them Galilean-invariant by deriving the criteria from a steady reference frame, rather than from a geometric characterization.
