Events

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.

Title: The size Ramsey numbers of graphs and hypergraphs

Abstract:

The s-colour size-Ramsey number of a graph ( hypergraph) H is the minimum number of edges in a graph (hypergraph) G whose every s-edge-colouring contains a monochromatic copy of H. While the   study of size Ramsey numbers for graphs  goes back to 70's to the work of Erdos, Faudree, Rousseau and Schelp, the systematic study of these numbers for hypergraphs have been initiated much more recently in 2017  by Dudek, La Fleur, Mubayi, and Rödl. In this talk we will present the current known results in the literature, and some recent progress we have made on several questions in the area.

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

Abstract:

Title: TBA

Abstract: 

Having briefly introduced the key ideas which make interior point methods (IPMs) such a powerful optimization approach, I shall focus on a solution of the Newton systems and in particular on the use of iterative (Krylov-subspace) techniques to perform this task.

The Newton systems arising in IPMs are inherently ill-conditioned and preconditioning is a must to make iterative methods work.

A re-design of IPMs to enable the use of iterative techniques provides a completely new perspective on these methods.

I will address both theoretical and practical aspects of it.

Title: HiGHS: Theory, software and Impact

Abstract:

Since Dantzig formulated the simplex algorithm in 1947, the widespread need to solve linear optimization problems drove the development of algorithmic and computational techniques for decades, yielding several high performance commercial and open source software systems. This talk will focus on the Edinburgh-based work on solving large scale sparse linear programming problems that underpins the high performance open source linear optimization software, HiGHS, the challenges of developing such software, and the Impact that it has achieved.

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: 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: 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: 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.