Journal of Global Optimization最新文献_第8页

The appeals of quadratic majorization–minimization 二次大化-最小化的诉求

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-28 DOI: 10.1007/s10898-023-01361-1

Marc C. Robini, Lihui Wang, Yuemin Zhu

引用次数: 0

Generalized derivatives of optimal-value functions with parameterized convex programs embedded 嵌入参数化凸程序的最优值函数广义导数

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-25 DOI: 10.1007/s10898-023-01359-9

Yingkai Song, Paul I. Barton

{"title":"Generalized derivatives of optimal-value functions with parameterized convex programs embedded","authors":"Yingkai Song, Paul I. Barton","doi":"10.1007/s10898-023-01359-9","DOIUrl":"https://doi.org/10.1007/s10898-023-01359-9","url":null,"abstract":"This article proposes new practical methods for furnishing generalized derivative information of optimal-value functions with embedded parameterized convex programs, with potential applications in nonsmooth equation-solving and optimization. We consider three cases of parameterized convex programs: (1) partial convexity—functions in the convex programs are convex with respect to decision variables for fixed values of parameters, (2) joint convexity—the functions are convex with respect to both decision variables and parameters, and (3) linear programs where the parameters appear in the objective function. These new methods calculate an LD-derivative, which is a recently established useful generalized derivative concept, by constructing and solving a sequence of auxiliary linear programs. In the general partial convexity case, our new method requires that the strong Slater conditions are satisfied for the embedded convex program’s decision space, and requires that the convex program has a unique optimal solution. It is shown that these conditions are essentially less stringent than the regularity conditions required by certain established methods, and our new method is at the same time computationally preferable over these methods. In the joint convexity case, the uniqueness requirement of an optimal solution is further relaxed, and to our knowledge, there is no established method for computing generalized derivatives prior to this work. In the linear program case, both the Slater conditions and the uniqueness of an optimal solution are not required by our new method.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A strong P-formulation for global optimization of industrial water-using and treatment networks 工业用水和水处理网络全局优化的强 P 公式

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-25 DOI: 10.1007/s10898-023-01363-z

Xin Cheng, Xiang Li

{"title":"A strong P-formulation for global optimization of industrial water-using and treatment networks","authors":"Xin Cheng, Xiang Li","doi":"10.1007/s10898-023-01363-z","DOIUrl":"https://doi.org/10.1007/s10898-023-01363-z","url":null,"abstract":"The problem of finding the optimal flow allocation within an industrial water-using and treatment network can be formulated into nonconvex nonlinear program or nonconvex mixed-integer nonlinear program. The efficiency of global optimization of the nonconvex program relies heavily on the strength of the problem formulation. In this paper, we propose a variant of the commonly used P-formulation, called the P(^*)-formulation, for the water treatment network (WTN) and the total water network (TWN) that includes water-using and water treatment units. For either type of networks, we prove that the P(^*)-formulation is at least as strong as the P-formulation under mild bound consistency conditions. We also prove for either type of networks that the P(^*)-formulation is at least as strong as the split-fraction based formulation (called SF-formulation) under certain bound consistency conditions. The computational study shows that the P(^*)-formulation significantly outperforms the P- and the SF-formulations. For some problem instances, the P(^*)-formulation is faster than the other two formulations by several orders of magnitudes.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

First- and second-order optimality conditions of nonsmooth sparsity multiobjective optimization via variational analysis 通过变分分析实现非光滑稀疏性多目标优化的一阶和二阶最优条件

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-22 DOI: 10.1007/s10898-023-01357-x

Jiawei Chen, Huasheng Su, Xiaoqing Ou, Yibing Lv

引用次数: 0

Interval constraint programming for globally solving catalog-based categorical optimization 基于目录的分类优化全局求解的区间约束编程

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-22 DOI: 10.1007/s10898-023-01362-0

引用次数: 0

Gaining or losing perspective for convex multivariate functions on a simplex 单纯形上凸多元函数的视角增减

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-22 DOI: 10.1007/s10898-023-01356-y

Luze Xu, Jon Lee

{"title":"Gaining or losing perspective for convex multivariate functions on a simplex","authors":"Luze Xu, Jon Lee","doi":"10.1007/s10898-023-01356-y","DOIUrl":"https://doi.org/10.1007/s10898-023-01356-y","url":null,"abstract":"MINLO (mixed-integer nonlinear optimization) formulations of the disjunction between the origin and a polytope via a binary indicator variable have broad applicability in nonlinear combinatorial optimization, for modeling a fixed cost c associated with carrying out a set of d activities and a convex variable cost function f associated with the levels of the activities. The perspective relaxation is often used to solve such models to optimality in a branch-and-bound context, especially in the context in which f is univariate (e.g., in Markowitz-style portfolio optimization). But such a relaxation typically requires conic solvers and are typically not compatible with general-purpose NLP software which can accommodate additional classes of constraints. This motivates the study of weaker relaxations to investigate when simpler relaxations may be adequate. Comparing the volume (i.e., Lebesgue measure) of the relaxations as means of comparing them, we lift some of the results related to univariate functions f to the multivariate case. Along the way, we survey, connect and extend relevant results on integration over a simplex, some of which we concretely employ, and others of which can be used for further exploration on our main subject.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust second order cone conditions and duality for multiobjective problems under uncertainty data 不确定数据下多目标问题的稳健二阶锥条件和对偶性

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-22 DOI: 10.1007/s10898-023-01335-3

Cao Thanh Tinh, Thai Doan Chuong

引用次数: 0

Single-lot, lot-streaming problem for a 1 + m hybrid flow shop 1 + m 混合流水车间的单批次、批次流水问题

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-12 DOI: 10.1007/s10898-023-01354-0

Sanchit Singh, Subhash C. Sarin, Ming Cheng

{"title":"Single-lot, lot-streaming problem for a 1 + m hybrid flow shop","authors":"Sanchit Singh, Subhash C. Sarin, Ming Cheng","doi":"10.1007/s10898-023-01354-0","DOIUrl":"https://doi.org/10.1007/s10898-023-01354-0","url":null,"abstract":"In this paper, we consider an application of lot-streaming for processing a lot of multiple items in a hybrid flow shop (HFS) for the objective of minimizing makespan. The HFS that we consider consists of two stages with a single machine available for processing in Stage 1 and m identical parallel machines in Stage 2. We call this problem a 1 + m TSHFS-LSP (two-stage hybrid flow shop, lot streaming problem), and show it to be NP-hard in general, except for the case when the sublot sizes are treated to be continuous. The novelty of our work is in obtaining closed-form expressions for optimal continuous sublot sizes that can be solved in polynomial time, for a given number of sublots. A fast linear search algorithm is also developed for determining the optimal number of sublots for the case of continuous sublot sizes. For the case when the sublot sizes are discrete, we propose a branch-and-bound-based heuristic to determine both the number of sublots and sublot sizes and demonstrate its efficacy by comparing its performance against that of a direct solution of a mixed-integer formulation of the problem by CPLEX®.","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On semidefinite programming relaxations for a class of robust SOS-convex polynomial optimization problems 关于一类鲁棒 SOS-凸多项式优化问题的半有限编程松弛

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-06 DOI: 10.1007/s10898-023-01353-1

Xiangkai Sun, Jiayi Huang, Kok Lay Teo

引用次数: 0

Spectral projected subgradient method with a 1-memory momentum term for constrained multiobjective optimization problem 针对受约束多目标优化问题的带 1 个内存动量项的频谱投射子梯度法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-05 DOI: 10.1007/s10898-023-01349-x

Jing-jing Wang, Li-ping Tang, Xin-min Yang

引用次数: 0