{"title":"Generalized scaling for the constrained maximum-entropy sampling problem","authors":"Zhongzhu Chen, Marcia Fampa, Jon Lee","doi":"10.1007/s10107-024-02101-3","DOIUrl":"https://doi.org/10.1007/s10107-024-02101-3","url":null,"abstract":"<p>The best practical techniques for exact solution of instances of the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a branch-and-bound framework, working with a variety of concave continuous relaxations of the objective function. A standard and computationally-important bound-enhancement technique in this context is <i>(ordinary) scaling</i>, via a single positive parameter. Scaling adjusts the shape of continuous relaxations to reduce the gaps between the upper bounds and the optimal value. We extend this technique to <i>generalized scaling</i>, employing a positive vector of parameters, which allows much more flexibility and thus potentially reduces the gaps further. We give mathematical results aimed at supporting algorithmic methods for computing optimal generalized scalings, and we give computational results demonstrating the performance of generalized scaling on benchmark problem instances.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"25 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503533","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}
{"title":"A PTAS for the horizontal rectangle stabbing problem","authors":"Arindam Khan, Aditya Subramanian, Andreas Wiese","doi":"10.1007/s10107-024-02106-y","DOIUrl":"https://doi.org/10.1007/s10107-024-02106-y","url":null,"abstract":"<p>We study rectangle stabbing problems in which we are given <i>n</i> axis-aligned rectangles in the plane that we want to <i>stab</i>, that is, we want to select line segments such that for each given rectangle there is a line segment that intersects two opposite edges of it. In the <i>horizontal rectangle stabbing problem</i> (<span>Stabbing</span>), the goal is to find a set of horizontal line segments of minimum total length such that all rectangles are stabbed. In the <i>horizontal–vertical stabbing problem</i> (<span>HV-Stabbing</span>), the goal is to find a set of rectilinear (that is, either vertical or horizontal) line segments of minimum total length such that all rectangles are stabbed. Both variants are NP-hard. Chan et al. (ISAAC, 2018) initiated the study of these problems by providing constant approximation algorithms. Recently, Eisenbrand et al. (A QPTAS for stabbing rectangles, 2021) have presented a QPTAS and a polynomial-time 8-approximation algorithm for <span>Stabbing</span>, but it was open whether the problem admits a PTAS. In this paper, we obtain a PTAS for <span>Stabbing</span>, settling this question. For <span>HV-Stabbing</span>, we obtain a <span>((2+varepsilon ))</span>-approximation. We also obtain PTASs for special cases of <span>HV-Stabbing</span>: (i) when all rectangles are squares, (ii) when each rectangle’s width is at most its height, and (iii) when all rectangles are <span>(delta )</span>-large, that is, have at least one edge whose length is at least <span>(delta )</span>, while all edge lengths are at most 1. Our result also implies improved approximations for other problems such as <i>generalized minimum Manhattan network</i>.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"46 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503476","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}
{"title":"An asynchronous proximal bundle method","authors":"Frank Fischer","doi":"10.1007/s10107-024-02088-x","DOIUrl":"https://doi.org/10.1007/s10107-024-02088-x","url":null,"abstract":"<p>We develop a fully asynchronous proximal bundle method for solving non-smooth, convex optimization problems. The algorithm can be used as a drop-in replacement for classic bundle methods, i.e., the function must be given by a first-order oracle for computing function values and subgradients. The algorithm allows for an arbitrary number of master problem processes computing new candidate points and oracle processes evaluating functions at those candidate points. These processes share information by communication with a single supervisor process that resembles the main loop of a classic bundle method. All processes run in parallel and no explicit synchronization step is required. Instead, the asynchronous and possibly outdated results of the oracle computations can be seen as an inexact function oracle. Hence, we show the convergence of our method under weak assumptions very similar to inexact and incremental bundle methods. In particular, we show how the algorithm learns important structural properties of the functions to control the inaccuracy induced by the asynchronicity automatically such that overall convergence can be guaranteed.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"13 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251916","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}
{"title":"A unified framework for symmetry handling","authors":"Jasper van Doornmalen, Christopher Hojny","doi":"10.1007/s10107-024-02102-2","DOIUrl":"https://doi.org/10.1007/s10107-024-02102-2","url":null,"abstract":"<p>Handling symmetries in optimization problems is essential for devising efficient solution methods. In this article, we present a general framework that captures many of the already existing symmetry handling methods. While these methods are mostly discussed independently from each other, our framework allows to apply different methods simultaneously and thus outperforming their individual effect. Moreover, most existing symmetry handling methods only apply to binary variables. Our framework allows to easily generalize these methods to general variable types. Numerical experiments confirm that our novel framework is superior to the state-of-the-art symmetry handling methods as implemented in the solver <span>SCIP</span> on a broad set of instances.\u0000</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"33 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251921","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}
{"title":"Universal heavy-ball method for nonconvex optimization under Hölder continuous Hessians","authors":"Naoki Marumo, Akiko Takeda","doi":"10.1007/s10107-024-02100-4","DOIUrl":"https://doi.org/10.1007/s10107-024-02100-4","url":null,"abstract":"<p>We propose a new first-order method for minimizing nonconvex functions with Lipschitz continuous gradients and Hölder continuous Hessians. The proposed algorithm is a heavy-ball method equipped with two particular restart mechanisms. It finds a solution where the gradient norm is less than <span>(varepsilon )</span> in <span>(O(H_{nu }^{frac{1}{2 + 2 nu }} varepsilon ^{- frac{4 + 3 nu }{2 + 2 nu }}))</span> function and gradient evaluations, where <span>(nu in [0, 1])</span> and <span>(H_{nu })</span> are the Hölder exponent and constant, respectively. This complexity result covers the classical bound of <span>(O(varepsilon ^{-2}))</span> for <span>(nu = 0)</span> and the state-of-the-art bound of <span>(O(varepsilon ^{-7/4}))</span> for <span>(nu = 1)</span>. Our algorithm is <span>(nu )</span>-independent and thus universal; it automatically achieves the above complexity bound with the optimal <span>(nu in [0, 1])</span> without knowledge of <span>(H_{nu })</span>. In addition, the algorithm does not require other problem-dependent parameters as input, including the gradient’s Lipschitz constant or the target accuracy <span>(varepsilon )</span>. Numerical results illustrate that the proposed method is promising.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"51 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251840","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}
{"title":"The complexity of first-order optimization methods from a metric perspective","authors":"A. S. Lewis, Tonghua Tian","doi":"10.1007/s10107-024-02091-2","DOIUrl":"https://doi.org/10.1007/s10107-024-02091-2","url":null,"abstract":"<p>A central tool for understanding first-order optimization algorithms is the Kurdyka–Łojasiewicz inequality. Standard approaches to such methods rely crucially on this inequality to leverage sufficient decrease conditions involving gradients or subgradients. However, the KL property fundamentally concerns not subgradients but rather “slope”, a purely metric notion. By highlighting this view, and avoiding any use of subgradients, we present a simple and concise complexity analysis for first-order optimization algorithms on metric spaces. This subgradient-free perspective also frames a short and focused proof of the KL property for nonsmooth semi-algebraic functions.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"71 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252287","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}
{"title":"Polarized consensus-based dynamics for optimization and sampling","authors":"Leon Bungert, Tim Roith, Philipp Wacker","doi":"10.1007/s10107-024-02095-y","DOIUrl":"https://doi.org/10.1007/s10107-024-02095-y","url":null,"abstract":"<p>In this paper we propose polarized consensus-based dynamics in order to make consensus-based optimization (CBO) and sampling (CBS) applicable for objective functions with several global minima or distributions with many modes, respectively. For this, we “polarize” the dynamics with a localizing kernel and the resulting model can be viewed as a bounded confidence model for opinion formation in the presence of common objective. Instead of being attracted to a common weighted mean as in the original consensus-based methods, which prevents the detection of more than one minimum or mode, in our method every particle is attracted to a weighted mean which gives more weight to nearby particles. We prove that in the mean-field regime the polarized CBS dynamics are unbiased for Gaussian targets. We also prove that in the zero temperature limit and for sufficiently well-behaved strongly convex objectives the solution of the Fokker–Planck equation converges in the Wasserstein-2 distance to a Dirac measure at the minimizer. Finally, we propose a computationally more efficient generalization which works with a predefined number of clusters and improves upon our polarized baseline method for high-dimensional optimization.\u0000</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"239 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189696","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}
{"title":"Exploiting the polyhedral geometry of stochastic linear bilevel programming","authors":"Gonzalo Muñoz, David Salas, Anton Svensson","doi":"10.1007/s10107-024-02097-w","DOIUrl":"https://doi.org/10.1007/s10107-024-02097-w","url":null,"abstract":"<p>We study linear bilevel programming problems whose lower-level objective is given by a random cost vector with known distribution. We consider the case where this distribution is nonatomic, allowing to reformulate the problem of the leader using the Bayesian approach in the sense of Salas and Svensson (SIAM J Optim 33(3):2311–2340, 2023), with a decision-dependent distribution that concentrates on the vertices of the feasible set of the follower’s problem. We call this a vertex-supported belief. We prove that this formulation is piecewise affine over the so-called chamber complex of the feasible set of the high-point relaxation. We propose two algorithmic approaches to solve general problems enjoying this last property. The first one is based on enumerating the vertices of the chamber complex. This approach is not scalable, but we present it as a computational baseline and for its theoretical interest. The second one is a Monte-Carlo approximation scheme based on the fact that randomly drawn points of the domain lie, with probability 1, in the interior of full-dimensional chambers, where the problem (restricted to this chamber) can be reduced to a linear program. Finally, we evaluate these methods through computational experiments showing both approaches’ advantages and challenges.\u0000</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"70 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141171635","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}
{"title":"ReLU neural networks of polynomial size for exact maximum flow computation","authors":"Christoph Hertrich, Leon Sering","doi":"10.1007/s10107-024-02096-x","DOIUrl":"https://doi.org/10.1007/s10107-024-02096-x","url":null,"abstract":"<p>This paper studies the expressive power of artificial neural networks with rectified linear units. In order to study them as a model of <i>real-valued</i> computation, we introduce the concept of <i>Max-Affine Arithmetic Programs</i> and show equivalence between them and neural networks concerning natural complexity measures. We then use this result to show that two fundamental combinatorial optimization problems can be solved with polynomial-size neural networks. First, we show that for any undirected graph with <i>n</i> nodes, there is a neural network (with fixed weights and biases) of size <span>(mathcal {O}(n^3))</span> that takes the edge weights as input and computes the value of a minimum spanning tree of the graph. Second, we show that for any directed graph with <i>n</i> nodes and <i>m</i> arcs, there is a neural network of size <span>(mathcal {O}(m^2n^2))</span> that takes the arc capacities as input and computes a maximum flow. Our results imply that these two problems can be solved with strongly polynomial time algorithms that solely use affine transformations and maxima computations, but no comparison-based branchings.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"104 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141152122","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}
{"title":"A tight $$(1.5+epsilon )$$ -approximation for unsplittable capacitated vehicle routing on trees","authors":"Claire Mathieu, Hang Zhou","doi":"10.1007/s10107-024-02094-z","DOIUrl":"https://doi.org/10.1007/s10107-024-02094-z","url":null,"abstract":"<p>In the unsplittable capacitated vehicle routing problem (UCVRP) on trees, we are given a rooted tree with edge weights and a subset of vertices of the tree called terminals. Each terminal is associated with a positive demand between 0 and 1. The goal is to find a minimum length collection of tours starting and ending at the root of the tree such that the demand of each terminal is covered by a single tour (i.e., the demand cannot be split), and the total demand of the terminals in each tour does not exceed the capacity of 1.</p><p>For the special case when all terminals have equal demands, a long line of research culminated in a quasi-polynomial time approximation scheme [Jayaprakash and Salavatipour, TALG 2023] and a polynomial time approximation scheme [Mathieu and Zhou, TALG 2023].</p><p>In this work, we study the general case when the terminals have arbitrary demands. Our main contribution is a polynomial time <span>((1.5+epsilon ))</span>-approximation algorithm for the UCVRP on trees. This is the first improvement upon the 2-approximation algorithm more than 30 years ago. Our approximation ratio is essentially best possible, since it is NP-hard to approximate the UCVRP on trees to better than a 1.5 factor.</p>","PeriodicalId":18297,"journal":{"name":"Mathematical Programming","volume":"75 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153885","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}