# Events

Over the past 10 years there has been a growing body of work at the intersection of mathematical programming as commonly studied in Operations Research and constraint programming (CP) with its origins in Artificial Intelligence (AI) and programming languages. Much of CP's success in solving challenging combinatorial problems comes from the exploitation of inference as... Read More

A major challenge for machine learning in the next decade is the development of methods that continuously predict and learn from a stream of data. The scale of data and the non-stationary nature of the environment make the prevalent "learn from a batch of i.i.d. data" paradigm inadequate. In this talk, we will formally define the problem of sequential prediction as a (... Read More

Bayesian statistical models can be used to represent the beliefs of a decision-maker about an uncertain environment. For example, in revenue management, a seller formulates beliefs about customers' willingness to pay; in energy, we may have a belief about the suitability of a candidate location for a new wind farm. Conjugate priors model the evolution of these beliefs... Read More

For efficient and physically reliable numerical computations for time dependent differential equations it is very important to find positively invariant subsets of the state space and determine time step sizes of the numerical method that guarantee this. In this talk, we present the state-of-the-art theory and novel approaches beyond it to find positively invariant... Read More

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