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Title: Towards LargeScale Nonconvex/Stochastic Discrete Optimization
Speaker: Cong Han Lim Affiliation: University of WisconsinMadison Room: MC 5501Abstract:
Modern data analytics is powered by scalable mathematical optimization methods. For decisionmaking, we want to be able to solve largescale mathematical problems that include discrete choices or structures. These can already be very challenging to solve exactly even when the objective and feasible region are convex.
Title: Nextgeneration authentication and key exchange protocols
Speaker: Douglas Stebila Affiliation: McMaster University Room: (change of room!) MC 5417Abstract:
Key exchange and authentication are at the heart of protocols for establishing secure communication on the Internet and other communication channels. In this talk, I'll discuss ways in which key exchange is evolving to meet new security demands and deliver new functionality.
Title: Stochastic algorithms for distributed optimization and machine learning
Speaker: Yi Zhou Affiliation: Georgia Institute of Technology Room: MC 5501Abstract:
In the big data era, machine learning acts as a powerful tool to transform data into knowledge that helps us make predictions and decisions. It has strong ties to the field of optimization, in the way the latter provides methods and theory.
Title: Quantum Latin Squares and Magic Unitaries
Speaker: Chris Godsil Affiliation: University of Waterloo Room: MC 6486Abstract:
A quantum Latin square is an n x n array of vectors such that vectors in any row or any column form an orthonormal basis for Cn. A magic unitary matrix is an n x n array of projections such that each row and column sums to I.
Title: Are cryptosystems based on ideal lattices quantumsafe ?
Speaker JeanFrancois Biasse Affiliation: University of South Florida Room: QNC 1501Abstract:
Shor's algorithm factors RSA integers and solves the Discrete Logarithm Problem (DLP) in quantum polynomial time. Therefore, alternatives to these cryptosystems must be developed to replace the current cryptographic schemes. One of the most interesting family of schemes that have been proposed for the replacement of RSAbased and DLPbased primitives relies on the hardness of finding short vectors in Euclidean lattices.
Title: Principled algorithms for finding local minima
Speaker: Oliver Hinder Affiliation: Stanford University Room: MC 5501Abstract:
Convex optimization is the cornerstone of continuous optimization, but many real problems are nonconvex: neural networks, airplane design, water network management, etc. This two part talk explores my work developing algorithms for finding local minima of nonconvex functions.
Title: Reading Seminar "Robust Optimization"
Speaker: Group Reading Affiliation: University of Waterloo Room: MC 5479Abstract:
In this reading seminar, we will go through Section 1. 21. 3 of Chapter 1 in the book 'Robust Optimization'
Title: TypeII Matrices
Speaker Chris Godsil Affiliation: University of Waterloo Room MC 6486Abstract:
The Schur product M o N of two matrices M and N is the usual entrywise product. The matrix N is the Schur inverse of M if M o N = J. Denote the Schur inverse of M by M(). An n x n matrix is a typeII matrix if
WW()T = nI.
Title: Sparse graphs with forbidden induced subgraphs and the ErdosHajnal conjecture
Speaker: Sophie Spirkl Affiliation: Princeton University Room: MC 5501Abstract:
A graph G is called Hfree if it does not contain H as an induced subgraph, i.e. H cannot be obtained from G by deleting vertices. A famous conjecture due to Erdos and Hajnal states that for every graph H, there is a constant c > 0 such that in every nvertex Hfree graph G, there is a set of nc vertices that are either all pairwise adjacent or all pairwise nonadjacent.
Title: The DouglasRachford splitting algorithm for inconsistent minimization problems
Speaker: Walaa Moursi Affiliation: Stanford University Room: MC 5417Abstract:
The DouglasRachford (DR) method is one of the most popular splitting methods in optimization. The method was first introduced in 1956 to numerically solve certain types of heat equations.
Title: Robust counterpart approximations of scalar chance constraints
Speaker: Haesol Im Affiliation: University of waterloo Room: MC 5479Abstract:
We will cover section 2.1, section 2.2 and part of section 2.3 of the book Robust Optimization by BenTal et al.
Please email any errors or updates to our website support/editor.
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