25,505 research outputs found
Labeling of graphs, sumset of squares of units modulo n and resonance varieties of matroids
This thesis investigates problems in a number of different areas of graph theory and its applications in other areas of mathematics. Motivated by the 1-2-3-Conjecture, we consider the closed distinguishing number of a graph G, denoted by dis[G]. We provide new upper bounds for dis[G] by using the Combinatorial Nullstellensatz. We prove that it is NP-complete to decide for a given planar subcubic graph G, whether dis[G] = 2. We show that for each integer t there is a bipartite graph G such that dis[G] \u3e t. Then some polynomial time algorithms and NP-hardness results for the problem of partitioning the edges of a graph into regular and/or locally irregular subgraphs are presented. We then move on to consider Johnson graphs to find resonance varieties of some classes of sparse paving matroids. The last application we consider is in number theory, where we find the number of solutions of the equation x21 + _ _ _ + x2 k = c, where c 2 Zn, and xi are all units in the ring Zn. Our approach is combinatorial using spectral graph theory
Discovery of low-dimensional structure in high-dimensional inference problems
Many learning and inference problems involve high-dimensional data such as images, video or genomic data, which cannot be processed efficiently using conventional methods due to their dimensionality. However, high-dimensional data often exhibit an inherent low-dimensional structure, for instance they can often be represented sparsely in some basis or domain. The discovery of an underlying low-dimensional structure is important to develop more robust and efficient analysis and processing algorithms.
The first part of the dissertation investigates the statistical complexity of sparse recovery problems, including sparse linear and nonlinear regression models, feature selection and graph estimation. We present a framework that unifies sparse recovery problems and construct an analogy to channel coding in classical information theory. We perform an information-theoretic analysis to derive bounds on the number of samples required to reliably recover sparsity patterns independent of any specific recovery algorithm. In particular, we show that sample complexity can be tightly characterized using a mutual information formula similar to channel coding results. Next, we derive major extensions to this framework, including dependent input variables and a lower bound for sequential adaptive recovery schemes, which helps determine whether adaptivity provides performance gains. We compute statistical complexity bounds for various sparse recovery problems, showing our analysis improves upon the existing bounds and leads to intuitive results for new applications.
In the second part, we investigate methods for improving the computational complexity of subgraph detection in graph-structured data, where we aim to discover anomalous patterns present in a connected subgraph of a given graph. This problem arises in many applications such as detection of network intrusions, community detection, detection of anomalous events in surveillance videos or disease outbreaks. Since optimization over connected subgraphs is a combinatorial and computationally difficult problem, we propose a convex relaxation that offers a principled approach to incorporating connectivity and conductance constraints on candidate subgraphs. We develop a novel nearly-linear time algorithm to solve the relaxed problem, establish convergence and consistency guarantees and demonstrate its feasibility and performance with experiments on real networks
On combinatorial optimisation in analysis of protein-protein interaction and protein folding networks
Abstract: Protein-protein interaction networks and protein folding networks represent prominent research topics at the intersection of bioinformatics and network science. In this paper, we present a study of these networks from combinatorial optimisation point of view. Using a combination of classical heuristics and stochastic optimisation techniques, we were able to identify several interesting combinatorial properties of biological networks of the COSIN project. We obtained optimal or near-optimal solutions to maximum clique and chromatic number problems for these networks. We also explore patterns of both non-overlapping and overlapping cliques in these networks. Optimal or near-optimal solutions to partitioning of these networks into non-overlapping cliques and to maximum independent set problem were discovered. Maximal cliques are explored by enumerative techniques. Domination in these networks is briefly studied, too. Applications and extensions of our findings are discussed
A Faster Combinatorial Algorithm for Maximum Bipartite Matching
The maximum bipartite matching problem is among the most fundamental and
well-studied problems in combinatorial optimization. A beautiful and celebrated
combinatorial algorithm of Hopcroft and Karp (1973) shows that maximum
bipartite matching can be solved in time on a graph with
vertices and edges. For the case of very dense graphs, a fast matrix
multiplication based approach gives a running time of . These
results represented the fastest known algorithms for the problem until 2013,
when Madry introduced a new approach based on continuous techniques achieving
much faster runtime in sparse graphs. This line of research has culminated in a
spectacular recent breakthrough due to Chen et al. (2022) that gives an
time algorithm for maximum bipartite matching (and more generally,
for min cost flows).
This raises a natural question: are continuous techniques essential to
obtaining fast algorithms for the bipartite matching problem? Our work makes
progress on this question by presenting a new, purely combinatorial algorithm
for bipartite matching, that runs in time, and
hence outperforms both Hopcroft-Karp and the fast matrix multiplication based
algorithms on moderately dense graphs. Using a standard reduction, we also
obtain an time deterministic algorithm for maximum
vertex-capacitated - flow in directed graphs when all vertex capacities
are identical
Combinatorial approach to the interpolation method and scaling limits in sparse random graphs
We establish the existence of free energy limits for several combinatorial
models on Erd\"{o}s-R\'{e}nyi graph and
random -regular graph . For a variety of models, including
independent sets, MAX-CUT, coloring and K-SAT, we prove that the free energy
both at a positive and zero temperature, appropriately rescaled, converges to a
limit as the size of the underlying graph diverges to infinity. In the zero
temperature case, this is interpreted as the existence of the scaling limit for
the corresponding combinatorial optimization problem. For example, as a special
case we prove that the size of a largest independent set in these graphs,
normalized by the number of nodes converges to a limit w.h.p. This resolves an
open problem which was proposed by Aldous (Some open problems) as one of his
six favorite open problems. It was also mentioned as an open problem in several
other places: Conjecture 2.20 in Wormald [In Surveys in Combinatorics, 1999
(Canterbury) (1999) 239-298 Cambridge Univ. Press]; Bollob\'{a}s and Riordan
[Random Structures Algorithms 39 (2011) 1-38]; Janson and Thomason [Combin.
Probab. Comput. 17 (2008) 259-264] and Aldous and Steele [In Probability on
Discrete Structures (2004) 1-72 Springer].Comment: Published in at http://dx.doi.org/10.1214/12-AOP816 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Structured Sparsity: Discrete and Convex approaches
Compressive sensing (CS) exploits sparsity to recover sparse or compressible
signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity
is also used to enhance interpretability in machine learning and statistics
applications: While the ambient dimension is vast in modern data analysis
problems, the relevant information therein typically resides in a much lower
dimensional space. However, many solutions proposed nowadays do not leverage
the true underlying structure. Recent results in CS extend the simple sparsity
idea to more sophisticated {\em structured} sparsity models, which describe the
interdependency between the nonzero components of a signal, allowing to
increase the interpretability of the results and lead to better recovery
performance. In order to better understand the impact of structured sparsity,
in this chapter we analyze the connections between the discrete models and
their convex relaxations, highlighting their relative advantages. We start with
the general group sparse model and then elaborate on two important special
cases: the dispersive and the hierarchical models. For each, we present the
models in their discrete nature, discuss how to solve the ensuing discrete
problems and then describe convex relaxations. We also consider more general
structures as defined by set functions and present their convex proxies.
Further, we discuss efficient optimization solutions for structured sparsity
problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure
Combinatorial theorems relative to a random set
We describe recent advances in the study of random analogues of combinatorial
theorems.Comment: 26 pages. Submitted to Proceedings of the ICM 201
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