Seminar

Title: Tails and Chains

Abstract:

Physicists are interested in "graphs with tails"; these are constructed by choosing a graph X and a subset C of its vertices, then attaching a path of length n to each vertex in C. We ask what is the spectrum of such graph? What happen if n increases? We will see that the answer reduces to questions about the matrix

\[ M(\zeta) := (\zeta_\zeta^{-1})I - A -\zeta D \]

where D is the diagonal 01-matrix with D_{i,i}=1 if i is in C.

Title: Structured (In)Feasibility: Nonmonotone Operator Splitting in Nonlinear Spaces

Abstract:

The success of operator splitting techniques for convex optimization has led to an explosion of methods for solving large-scale and nonconvex optimization problems via convex relaxation. This success is at the cost of overlooking direct approaches to operator splitting that embrace some of the more inconvenient aspects of many model problems, namely nonconvexity, nonsmoothness, and infeasibility.

Title: Approximation Algorithms for Matchings in Big Graphs

Abstract:

Matchings in graphs are classical problems in combinatorial optimization and computer science, significant due to their theoretical importance and relevance to applications. Polynomial time algorithms for several variant matching problems with linear objective functions have been known for fifty years, with important contributions from Tutte, Edmonds, Cunningham, and Pulleyblank (all with Waterloo associations), and they are discussed in the textbook literature.

Title: On solving bilevel programming problems

Abstract:

A bilevel programming problem is a sequence of two optimization problems where the constraint region of the upper level problem is determined implicitly by the solution set to the lower level problem. It can be used to model a two-level hierarchical system where the two decision makers have different objectives and make their decisions on different levels of hierarchy. Recently more and more applications including those in machine learning have been modelled as bilevel optimization problems. In this talk, I will report some recent developments in optimality conditions and numerical algorithms for solving this class of very difficult optimization problems.

Title: Chromatic structure in locally sparse graphs

Abstract:

Efforts to understand the structure of triangle-free graphs have long played a central role in the development of combinatorial mathematics. Classic results of Mantel (1907), Ramsey (1930), Blanche Descartes (1948) produce global structure from the local property of having no edges in any neighbourhood. Despite the scrutiny it has sustained over the decades, study of this topic, and its close relatives, continues to uncover surprisingly basic challenges, insights and connections. I will give a brief overview of the history as well as the focus of current/recent momentum.

Title: Factorization of completely positive matrices using iterative projected gradient steps

Abstract:

We aim to factorize a completely positive matrix by using an optimization approach which consists in the minimization of a nonconvex smooth function over a convex and compact set. To solve this problem we propose a projected gradient algorithm with parameters that take into account the effects of relaxation and inertia. Both projection and gradient steps are simple in the sense that they have explicit formulas and do not require inner loops. We show that the sequence of generated iterates

Title: Spectral Properties of the eccentricity matrix for special classes of graphs

Abstract:

Eccentricity matrix, another graph matrix, was originally proposed, as $D_{MAX}$ matrix, by Randi\'c in 2013 and redefined by Wang et al. in 2018 by using the concept of the eccentricities of vertices.

Title: Skew Adjacency Matrices:  The Number of Characteristic polynomials of Skew Cacti

Abstract:

Title: Dyadic Linear Programming

Abstract:

Most linear programming solvers use fixed-precision floating points to approximate the rational numbers. Though successful on most real-world instances, solvers sometimes run into serious issues when carrying out sequential floating-point arithmetic, due to compounded error terms. This practical limitation leads to the following theoretical problem:

Title: The average search probability in a quantum walk with an oracle

Abstract:

Some quantum search algorithms can be viewed as discrete-time quantum walks on graphs with a marked vertex a. In such a walk, the oracle is part of the transition matrix, the target state is the characteristic vector of the outgoing arcs of a, and the initial state is the all-ones vector.