# Events

For all-quadratic problems (without any linear constraints), it is well known that the semidefinite relaxation coincides basically with the Lagrangian dual problem. Here we study a more general case where the constraints can be either quadratic or linear. To be more precise, we include explicit sign constraints on the problem variables, and study both the full... Read More

The first part of this talk reviews some modern randomized linear algebra techniques. The goal of these methods is to perform approximate matrix multiplication or matrix factorizations (e.g., SVD) with lower computational cost than conventional methods. We then discuss using these methods inside optimization algorithms. The two main questions are (1) is the randomized... Read More

**Public Lecture - "New Goals for American Corporations"**

What should we expect of our great American Corporations? Is the present dominant corporate goal of maximizing return to the shareholders the right answer to that question? A historical view suggests that, for the country as a whole, the current goal may not be the right answer and suggests... Read More

The role of hospital bed management staff and processes has gained increased attention in recent years due to the impact of bed management practices on hospital performance metrics including average boarding time, patient safety, overflow rate, and patient diversions. One of the key tasks of the bed manager is to balance the available capacity with competing requests... Read More

A symmetric matrix is called copositive, if it generates a quadratic form taking no negative values over the positive orthant. Contrasting to positive-semideniteness, checking copositivity is NP-hard. In a copositive optimization problem, we have to minimize a linear function of a symmetric matrix over the copositive cone subject to linear constraints. This convex... Read More

We will explore the development of efficient batch optimization algorithms for solving large-scale statistical learning applications; particularly those that can be formulated as a nonlinear programming problem. We rst investigate smooth, unconstrained problems, with applications in speech recognition. To reduce the computational cost of the optimization process, we... Read More

We study the design of reliably connected networks. Given a graph with arcs that may fail at random, the goal is to select a minimum cost set of arcs such that a connectivity requirement is met with high probability. We first compare this model with a well-known deterministic model of reliable network design: survivable network design. We demonstrate that, if... Read More

Set functions, i.e., real mappings form the family of subsets of a nite set to the reals are known and widely used in discrete mathematics for almost a century, and in particular in the last 50 years. If we replace a finite set with its characteristic vector, then the same set function can be interpreted as a mapping from the set of binary vectors to the reals. Such... Read More

The software systems commonly used to solve linear and integer programming problems today make use of oating-point computation and the inexactness of these computations can lead to errors in the returned results. Although such numerical errors are sometimes tolerable, there are situations when exact results are necessary. This talk will describe a variety of methods... Read More

How should the Centers for Disease Control and Prevention revise national immunization recommendations so that gaps in vaccination coverage will be filled in a cost-effective manner? What is the most cost-effective way to use limited HIV prevention and treatment resources? To what extent should local communities stockpile antibiotics for response to a potential... Read More