Mathematical Programming最新文献_第3页

A radial basis function method for noisy global optimisation 噪声全局优化的径向基函数方法

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-08-08 DOI: 10.1007/s10107-024-02125-9

Dirk Banholzer, Jörg Fliege, Ralf Werner

引用次数: 0

Optimizing distortion riskmetrics with distributional uncertainty 优化具有分布不确定性的扭曲风险度量

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-29 DOI: 10.1007/s10107-024-02128-6

Silvana M. Pesenti, Qiuqi Wang, Ruodu Wang

{"title":"Optimizing distortion riskmetrics with distributional uncertainty","authors":"Silvana M. Pesenti, Qiuqi Wang, Ruodu Wang","doi":"10.1007/s10107-024-02128-6","DOIUrl":"https://doi.org/10.1007/s10107-024-02128-6","url":null,"abstract":"Optimization of distortion riskmetrics with distributional uncertainty has wide applications in finance and operations research. Distortion riskmetrics include many commonly applied risk measures and deviation measures, which are not necessarily monotone or convex. One of our central findings is a unifying result that allows to convert an optimization of a non-convex distortion riskmetric with distributional uncertainty to a convex one induced from the concave envelope of the distortion function, leading to practical tractability. A sufficient condition to the unifying equivalence result is the novel notion of closedness under concentration, a variation of which is also shown to be necessary for the equivalence. Our results include many special cases that are well studied in the optimization literature, including but not limited to optimizing probabilities, Value-at-Risk, Expected Shortfall, Yaari’s dual utility, and differences between distortion risk measures, under various forms of distributional uncertainty. We illustrate our theoretical results via applications to portfolio optimization, optimization under moment constraints, and preference robust optimization.","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"48 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Convergence in distribution of randomized algorithms: the case of partially separable optimization 随机算法分布的收敛性：部分可分离优化的案例

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-27 DOI: 10.1007/s10107-024-02124-w

D. Russell Luke

引用次数: 0

On supervalid inequalities for binary interdiction games 论二元互斥博弈的监督不等式

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-27 DOI: 10.1007/s10107-024-02111-1

Ningji Wei, Jose L. Walteros

引用次数: 0

The pseudo-Boolean polytope and polynomial-size extended formulations for binary polynomial optimization 二元多项式优化的伪布尔多面体和多项式大小扩展公式

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-22 DOI: 10.1007/s10107-024-02122-y

Alberto Del Pia, Aida Khajavirad

引用次数: 0

A trust region-type normal map-based semismooth Newton method for nonsmooth nonconvex composite optimization 基于信任区域型法线图的非平滑非凸复合优化半平滑牛顿法

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-22 DOI: 10.1007/s10107-024-02110-2

Wenqing Ouyang, Andre Milzarek

引用次数: 0

Competitive kill-and-restart and preemptive strategies for non-clairvoyant scheduling 非千里眼调度的竞争性杀死-重启和抢先策略

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-22 DOI: 10.1007/s10107-024-02118-8

Sven Jäger, Guillaume Sagnol, Daniel Schmidt genannt Waldschmidt, Philipp Warode

{"title":"Competitive kill-and-restart and preemptive strategies for non-clairvoyant scheduling","authors":"Sven Jäger, Guillaume Sagnol, Daniel Schmidt genannt Waldschmidt, Philipp Warode","doi":"10.1007/s10107-024-02118-8","DOIUrl":"https://doi.org/10.1007/s10107-024-02118-8","url":null,"abstract":"We study kill-and-restart and preemptive strategies for the fundamental scheduling problem of minimizing the sum of weighted completion times on a single machine in the non-clairvoyant setting. First, we show a lower bound of 3 for any deterministic non-clairvoyant kill-and-restart strategy. Then, we give for any (b > 1) a tight analysis for the natural b-scaling kill-and-restart strategy as well as for a randomized variant of it. In particular, we show a competitive ratio of ((1+3sqrt{3})approx 6.197) for the deterministic and of (approx 3.032) for the randomized strategy, by making use of the largest eigenvalue of a Toeplitz matrix. In addition, we show that the preemptive Weighted Shortest Elapsed Time First (WSETF) rule is 2-competitive when jobs are released online, matching the lower bound for the unit weight case with trivial release dates for any non-clairvoyant algorithm. Using this result as well as the competitiveness of round-robin for multiple machines, we prove performance guarantees smaller than 10 for adaptions of the b-scaling strategy to online release dates and unweighted jobs on identical parallel machines.","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"26 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 the geometry and refined rate of primal–dual hybrid gradient for linear programming 论线性规划的初等-双重混合梯度的几何形状和精炼率

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-17 DOI: 10.1007/s10107-024-02109-9

Haihao Lu, Jinwen Yang

{"title":"On the geometry and refined rate of primal–dual hybrid gradient for linear programming","authors":"Haihao Lu, Jinwen Yang","doi":"10.1007/s10107-024-02109-9","DOIUrl":"https://doi.org/10.1007/s10107-024-02109-9","url":null,"abstract":"We study the convergence behaviors of primal–dual hybrid gradient (PDHG) for solving linear programming (LP). PDHG is the base algorithm of a new general-purpose first-order method LP solver, PDLP, which aims to scale up LP by taking advantage of modern computing architectures. Despite its numerical success, the theoretical understanding of PDHG for LP is still very limited; the previous complexity result relies on the global Hoffman constant of the KKT system, which is known to be very loose and uninformative. In this work, we aim to develop a fundamental understanding of the convergence behaviors of PDHG for LP and to develop a refined complexity rate that does not rely on the global Hoffman constant. We show that there are two major stages of PDHG for LP: in Stage I, PDHG identifies active variables and the length of the first stage is driven by a certain quantity which measures how close the non-degeneracy part of the LP instance is to degeneracy; in Stage II, PDHG effectively solves a homogeneous linear inequality system, and the complexity of the second stage is driven by a well-behaved local sharpness constant of the system. This finding is closely related to the concept of partial smoothness in non-smooth optimization, and it is the first complexity result of finite time identification without the non-degeneracy assumption. An interesting implication of our results is that degeneracy itself does not slow down the convergence of PDHG for LP, but near-degeneracy does.","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"30 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Distributional utility preference robust optimization models in multi-attribute decision making 多属性决策中的分配效用偏好稳健优化模型

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-17 DOI: 10.1007/s10107-024-02114-y

Jian Hu, Dali Zhang, Huifu Xu, Sainan Zhang

{"title":"Distributional utility preference robust optimization models in multi-attribute decision making","authors":"Jian Hu, Dali Zhang, Huifu Xu, Sainan Zhang","doi":"10.1007/s10107-024-02114-y","DOIUrl":"https://doi.org/10.1007/s10107-024-02114-y","url":null,"abstract":"Utility preference robust optimization (PRO) has recently been proposed to deal with optimal decision-making problems where the decision maker’s (DM’s) preference over gains and losses is ambiguous. In this paper, we take a step further to investigate the case that the DM’s preference is random. We propose to use a random utility function to describe the DM’s preference and develop distributional utility preference robust optimization (DUPRO) models when the distribution of the random utility function is ambiguous. We concentrate on data-driven problems where samples of the random parameters are obtainable but the sample size may be relatively small. In the case when the random utility functions are of piecewise linear structure, we propose a bootstrap method to construct the ambiguity set and demonstrate how the resulting DUPRO can be solved by a mixed-integer linear program. The piecewise linear structure is versatile in its ability to incorporate classical non-parametric utility assessment methods into the sample generation of a random utility function. Next, we expand the proposed DUPRO models and computational schemes to address general cases where the random utility functions are not necessarily piecewise linear. We show how the DUPRO models with piecewise linear random utility functions can serve as approximations for the DUPRO models with general random utility functions and allow us to quantify the approximation errors. Finally, we carry out some performance studies of the proposed bootstrap-based DUPRO model and report the preliminary numerical test results. This paper is the first attempt to use distributionally robust optimization methods for PRO problems.\u0000","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"31 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

New notions of simultaneous diagonalizability of quadratic forms with applications to QCQPs 二次型同时对角化的新概念及其在 QCQPs 中的应用

IF 2.7 2区数学

Mathematical Programming Pub Date : 2024-07-17 DOI: 10.1007/s10107-024-02120-0

Alex L. Wang, Rujun Jiang

{"title":"New notions of simultaneous diagonalizability of quadratic forms with applications to QCQPs","authors":"Alex L. Wang, Rujun Jiang","doi":"10.1007/s10107-024-02120-0","DOIUrl":"https://doi.org/10.1007/s10107-024-02120-0","url":null,"abstract":"A set of quadratic forms is simultaneously diagonalizable via congruence (SDC) if there exists a basis under which each of the quadratic forms is diagonal. This property appears naturally when analyzing quadratically constrained quadratic programs (QCQPs) and has important implications in globally solving such problems using branch-and-bound methods. This paper extends the reach of the SDC property by studying two new weaker notions of simultaneous diagonalizability. Specifically, we say that a set of quadratic forms is almost SDC (ASDC) if it is the limit of SDC sets and d-restricted SDC (d-RSDC) if it is the restriction of an SDC set in up to d-many additional dimensions. In the context of QCQPs, these properties correspond to problems that may be diagonalized after arbitrarily small perturbations or after the introduction of d additional variables. Our main contributions are complete characterizations of the ASDC pairs and nonsingular triples of symmetric matrices, as well as a sufficient condition for the 1-RSDC property for pairs of symmetric matrices. Surprisingly, we show that every singular symmetric pair is ASDC and that almost every symmetric pair is 1-RSDC. We accompany our theoretical results with preliminary numerical experiments applying these constructions to solve QCQPs within branch-and-bound schemes.","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"44 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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