Conference on Integer Programming and Combinatorial Optimization最新文献_第2页

Exploiting the Polyhedral Geometry of Stochastic Linear Bilevel Programming 利用随机线性双层规划的多面体几何

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-11-04 DOI: 10.1007/978-3-031-32726-1_26

G. Muñoz, David Salas, Anton Svensson

引用次数: 0

An Update-and-Stabilize Framework for the Minimum-Norm-Point Problem 最小范数点问题的更新与稳定框架

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-11-04 DOI: 10.1007/978-3-031-32726-1_11

S. Fujishige, Tomonari Kitahara, L. V'egh

引用次数: 1

Compressing Branch-and-Bound Trees 压缩分支绑定树

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-11-04 DOI: 10.1007/978-3-031-32726-1_25

Gonzalo Muñoz, Joseph Paat, Á. S. Xavier

引用次数: 0

The Polyhedral Geometry of Truthful Auctions 真实拍卖的多面体几何

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-11-03 DOI: 10.48550/arXiv.2211.01907

M. Joswig, Max Klimm, Sylvain Spitz

引用次数: 0

Set Selection under Explorable Stochastic Uncertainty via Covering Techniques 基于覆盖技术的可探索随机不确定性下的集合选择

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-11-02 DOI: 10.48550/arXiv.2211.01097

Nicole Megow, Jens Schloter

{"title":"Set Selection under Explorable Stochastic Uncertainty via Covering Techniques","authors":"Nicole Megow, Jens Schloter","doi":"10.48550/arXiv.2211.01097","DOIUrl":"https://doi.org/10.48550/arXiv.2211.01097","url":null,"abstract":"Given subsets of uncertain values, we study the problem of identifying the subset of minimum total value (sum of the uncertain values) by querying as few values as possible. This set selection problem falls into the field of explorable uncertainty and is of intrinsic importance therein as it implies strong adversarial lower bounds for a wide range of interesting combinatorial problems such as knapsack and matchings. We consider a stochastic problem variant and give algorithms that, in expectation, improve upon these adversarial lower bounds. The key to our results is to prove a strong structural connection to a seemingly unrelated covering problem with uncertainty in the constraints via a linear programming formulation. We exploit this connection to derive an algorithmic framework that can be used to solve both problems under uncertainty, obtaining nearly tight bounds on the competitive ratio. This is the first non-trivial stochastic result concerning the sum of unknown values without further structure known for the set. With our novel methods, we lay the foundations for solving more general problems in the area of explorable uncertainty.","PeriodicalId":421894,"journal":{"name":"Conference on Integer Programming and Combinatorial Optimization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122237247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A nearly optimal randomized algorithm for explorable heap selection 可探索堆选择的近乎最优的随机算法

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-10-12 DOI: 10.48550/arXiv.2210.05982

Sander Borst, D. Dadush, Sophie Huiberts, Danish Kashaev

引用次数: 1

Sparse Approximation Over the Cube 立方体上的稀疏逼近

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.02738

Sabrina Bruckmeier, Christoph Hunkenschröder, R. Weismantel

引用次数: 0

On the Correlation Gap of Matroids 关于拟阵的相关间隙

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-09-20 DOI: 10.48550/arXiv.2209.09896

Edin Husi'c, Zhuan Khye Koh, Georg Loho, L. V'egh

引用次数: 1

Optimal General Factor Problem and Jump System Intersection 最优一般因子问题与跳跃系统交集

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-09-02 DOI: 10.48550/arXiv.2209.00779

Yusuke Kobayashi

引用次数: 2

Configuration Balancing for Stochastic Requests 随机请求的配置均衡

Conference on Integer Programming and Combinatorial Optimization Pub Date : 2022-08-29 DOI: 10.48550/arXiv.2208.13702

Franziska Eberle, Anupam Gupta, Nicole Megow, Benjamin Moseley, Rudy Zhou

{"title":"Configuration Balancing for Stochastic Requests","authors":"Franziska Eberle, Anupam Gupta, Nicole Megow, Benjamin Moseley, Rudy Zhou","doi":"10.48550/arXiv.2208.13702","DOIUrl":"https://doi.org/10.48550/arXiv.2208.13702","url":null,"abstract":"The configuration balancing problem with stochastic requests generalizes many well-studied resource allocation problems such as load balancing and virtual circuit routing. In it, we have $m$ resources and $n$ requests. Each request has multiple possible configurations, each of which increases the load of each resource by some amount. The goal is to select one configuration for each request to minimize the makespan: the load of the most-loaded resource. In our work, we focus on a stochastic setting, where we only know the distribution for how each configuration increases the resource loads, learning the realized value only after a configuration is chosen. We develop both offline and online algorithms for configuration balancing with stochastic requests. When the requests are known offline, we give a non-adaptive policy for configuration balancing with stochastic requests that $O(frac{log m}{log log m})$-approximates the optimal adaptive policy. In particular, this closes the adaptivity gap for this problem as there is an asymptotically matching lower bound even for the very special case of load balancing on identical machines. When requests arrive online in a list, we give a non-adaptive policy that is $O(log m)$ competitive. Again, this result is asymptotically tight due to information-theoretic lower bounds for very special cases (e.g., for load balancing on unrelated machines). Finally, we show how to leverage adaptivity in the special case of load balancing on related machines to obtain a constant-factor approximation offline and an $O(log log m)$-approximation online. A crucial technical ingredient in all of our results is a new structural characterization of the optimal adaptive policy that allows us to limit the correlations between its decisions.","PeriodicalId":421894,"journal":{"name":"Conference on Integer Programming and Combinatorial Optimization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130202678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0