Class of ’27 Lecture Series

The Class of ’27 Lecture Series is a special lecture held each year. It was established in 1960 to honor the first chair of the Math Sciences Department, Professor Edwin Allen. The three members of the class of 1927 who established this series are Issac Arnold, Alexander Hassan, and Isadore Fixman.

 

 

 

Class of '27 Lecture II - "Randomness in phase estimation and Heisenberg-limited Hamiltonian learning"

Lexing Ying from Stanford University

Abstract: This talk presents some recent progress in using randomness in the design of efficient quantum algorithms. First, we consider new randomized algorithms for the robust quantum phase estimation problem. Next, we discuss its applications in Heisenberg-limited Hamiltonian learning for interacting bosons and Fermi-Hubbard models.

 

Class of '27 Lecture I - "Eigenmatrix for Unstructured Sparse Recovery"

Lexing Ying from Stanford University

Abstract: This talk considers the unstructured sparse recovery problems in a general form. Examples include rational approximation, spectral function estimation, Fourier inversion, Laplace inversion, and sparse deconvolution. We propose the eigenmatrix as a unified solution for these sparse recovery problems. The key is a data-driven construction with desired approximate eigenvalues and eigenvectors. We also discuss its multidimensional version and applications in free deconvolution.

 

Class of '27 Lecture II-"Convergence Analysis of Stochastic Optimization Methods via Martingales"

Katya Scheinberg from Lehigh University
Abstract:  We will present a very general framework for unconstrained stochastic optimization which encompasses standard frameworks such as line search and trust region using random models. In particular this framework retains the desirable practical features such step acceptance criterion, trust region adjustment and ability to utilize of second order models. The framework is based on bounding the expected stopping time of a stochastic process, which satisfies certain assumptions...

Class of '27 Lecture I-"Gradient Decent Without Gradients"

Katya Scheinberg from Lehigh University
Abstract: The core of continuous optimization lies in using information from first and second order derivatives to produce steps that improve objective function value. Classical methods such as gradient decent and Newton method rely on this information. The recently popular method in machine learning - Stochastic Gradient Decent - does not require the gradient itself, but still requires its unbiased estimate. However, in many applications either derivatives or their unbiased estimates are not available.

Class of ’27 Lecture : Coordinate Descent Methods

Stephen J. Wright from University of Wisconsin
Coordinate descent is an approach for minimizing functions in which only a subset of the variables are allowed to change at each iteration, while the remaining variables are held fixed. This approach has been popular in applications since the earliest days of optimization, because it is intuitive and because the low-dimensional searches that take place at each iteration are inexpensive in many applications. In recent years, the popularity of coordinate descent methods has grown further because of their usefulness in data analysis.

Class of ’27 Lecture: Fundamental Optimization Methods in Data Analysis

Stephen Wright from University of Wisconsin
Optimization formulations and algorithms are vital tools for solving problems in data analysis. There has been particular interest in some fundamental, elementary, optimization algorithms that were previously thought to have only niche appeal. Stochastic gradient, coordinate descent, and accelerated first-order methods are three examples. We outline applications in which these approaches are useful, discuss the basic properties of these methods, and survey some recent developments in the analysis of their convergence behavior.
Mathematical Sciences

Amos Eaton 301

110 8th Street

Troy, NY 12180

(518) 276-6345

Amos Eaton Hall
Contact Us
Back to top