How Do Exponential Size Solutions Arise in Semidefinite Programming?

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Optimization Pub Date : 2024-03-08 DOI:10.1137/21m1434945
Gábor Pataki, Aleksandr Touzov
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Abstract

SIAM Journal on Optimization, Volume 34, Issue 1, Page 977-1005, March 2024.
Abstract. A striking pathology of semidefinite programs (SDPs) is illustrated by a classical example of Khachiyan: feasible solutions in SDPs may need exponential space even to write down. Such exponential size solutions are the main obstacle to solving a long standing, fundamental open problem: can we decide feasibility of SDPs in polynomial time? The consensus seems that SDPs with large size solutions are rare. However, here we prove that they are actually quite common: a linear change of variables transforms every strictly feasible SDP into a Khachiyan type SDP, in which the leading variables are large. As to “how large,” that depends on the singularity degree of a dual problem. Further, we present some SDPs coming from sum-of-squares proofs, in which large solutions appear naturally, without any change of variables. We also partially answer the question how do we represent such large solutions in polynomial space?
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半定式编程中如何产生指数大小的解决方案?
SIAM 优化期刊》第 34 卷第 1 期第 977-1005 页,2024 年 3 月。 摘要。哈奇扬的一个经典例子说明了半无限程序(SDP)的一个显著病理:SDP 中的可行解甚至需要指数级的空间才能写下来。这种指数大小的解是解决一个长期存在的基本开放问题的主要障碍:我们能否在多项式时间内决定 SDP 的可行性?人们似乎一致认为,具有大尺寸解的 SDPs 很少见。然而,我们在这里证明,它们其实很常见:变量的线性变化会将每一个严格可行的 SDP 转化为哈奇扬类型的 SDP,其中前导变量都很大。至于 "有多大",这取决于对偶问题的奇异度。此外,我们还介绍了一些来自平方和证明的 SDP,在这些 SDP 中,无需改变变量,大解就会自然出现。我们还部分回答了如何在多项式空间中表示这种大解的问题?
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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