4 research outputs found
Column-Randomized Linear Programs: Performance Guarantees and Applications
We propose a randomized method for solving linear programs with a large
number of columns but a relatively small number of constraints. Since
enumerating all the columns is usually unrealistic, such linear programs are
commonly solved by column generation, which is often still computationally
challenging due to the intractability of the subproblem in many applications.
Instead of iteratively introducing one column at a time as in column
generation, our proposed method involves sampling a collection of columns
according to a user-specified randomization scheme and solving the linear
program consisting of the sampled columns. While similar methods for solving
large-scale linear programs by sampling columns (or, equivalently, sampling
constraints in the dual) have been proposed in the literature, in this paper we
derive an upper bound on the optimality gap that holds with high probability
and converges with rate , where is the number of sampled
columns, to the value of a linear program related to the sampling distribution.
To the best of our knowledge, this is the first paper addressing the
convergence of the optimality gap for sampling columns/constraints in generic
linear programs without additional assumptions on the problem structure and
sampling distribution. We further apply the proposed method to various
applications, such as linear programs with totally unimodular constraints,
Markov decision processes, covering problems and packing problems, and derive
problem-specific performance guarantees. We also generalize the method to the
case that the sampled columns may not be statistically independent. Finally, we
numerically demonstrate the effectiveness of the proposed method in the
cutting-stock problem and in nonparametric choice model estimation
Maximum likelihood estimation and graph matching in errorfully observed networks
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study this problem from a statistical framework in which one of the graphs is an errorfully observed copy of the other. We introduce a corrupting channel model, and show that in this model framework, the solution to the graph matching problem is a maximum likelihood estimator (MLE). Necessary and sufficient conditions for consistency of this MLE are presented, as well as a relaxed notion of consistency in which a negligible fraction of the vertices need not be matched correctly. The results are used to study matchability in several families of random graphs, including edge independent models, random regular graphs, and small-world networks. We also use these results to introduce measures of matching feasibility, and experimentally validate the results on simulated and real-world networks. Supplemental files for this article are available online.Accepted manuscrip
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
Contrastive learning is an efficient approach to self-supervised
representation learning. Although recent studies have made progress in the
theoretical understanding of contrastive learning, the investigation of how to
characterize the clusters of the learned representations is still limited. In
this paper, we aim to elucidate the characterization from theoretical
perspectives. To this end, we consider a kernel-based contrastive learning
framework termed Kernel Contrastive Learning (KCL), where kernel functions play
an important role when applying our theoretical results to other frameworks. We
introduce a formulation of the similarity structure of learned representations
by utilizing a statistical dependency viewpoint. We investigate the theoretical
properties of the kernel-based contrastive loss via this formulation. We first
prove that the formulation characterizes the structure of representations
learned with the kernel-based contrastive learning framework. We show a new
upper bound of the classification error of a downstream task, which explains
that our theory is consistent with the empirical success of contrastive
learning. We also establish a generalization error bound of KCL. Finally, we
show a guarantee for the generalization ability of KCL to the downstream
classification task via a surrogate bound