# Optimization

Tuesday, February 14, 2017 - 9:00am - 9:50am

Joyce Mclaughlin (Rensselaer Polytechnic Institute)

In biomechanical Imaging of tissue and in geophysics, viscoelastic models are used in order for the mathematical models: (1) to accurately predict the data; and (2) given the data, to enable the imaging functional to accurately compute biomechanical properties of tissue or physical properties of the earth. The mathematical structure of these integro-differential operators, in the time/space domain, have new properties.

Thursday, June 9, 2016 - 3:15pm - 4:15pm

Andrew Barker (Lawrence Livermore National Laboratory)

Optimization of controls and parameters coming from realistic full-scale simulation requires enormous computational effort. To make such optimization practical requires optimal multilevel solvers and scalable parallel algorithms. Even in the case where such solvers and algorithms are well understood for the forward problem, adapting them to the optimization context can be interesting and complicated.

Thursday, June 9, 2016 - 2:00pm - 3:00pm

Thomas Grandine (The Boeing Company)

PDE-constrained optimization is an essential technology for product development at The Boeing Company. This talk will survey three separate applications of increasing mathematical and computational complexity. The first application is a relatively straightforward parametric surface lofting application. The second application is structural analysis. In practice, there are at least three ways in which PDE-constrained optimization can be carried out, and these three approaches will all be reviewed.

Wednesday, June 8, 2016 - 11:15am - 12:15pm

Sven Leyffer (Argonne National Laboratory)

Many complex applications can be formulated as optimization problems constrained by partial differential equations (PDEs) with integer decision variables. Examples include the remediation of contaminated sites and the maximization of oil recovery; the design of next generation solar cells; the layout design of wind-farms; the design and control of gas networks; disaster recovery; and topology optimization.

Thursday, March 17, 2016 - 10:30am - 11:00am

Carlos Rautenberg (Humboldt-Universität)

We address the problem of optimally placing sensor networks for convection-diffusion processes where the convective part is perturbed. The problem is formulated as an optimal control problem where the integral Riccati equation is a constraint and the design variables are sensor locations. The objective functional involves a term associated to the trace of the solution to the Riccati equation and a term given by an additional constrained optimization problem subject to a set of admissible perturbations.

Tuesday, March 15, 2016 - 10:30am - 11:00am

Chunming Wang (University of Southern California)

Assimilation of observation data in meteorology and space weather consists of using these data to estimate the current state and the spatially and temporally distributed parameters of Numerical Weather Prediction (NWP) models, which are often fluid dynamical equations. The aim of the data assimilation is to provide wider monitoring of the weather condition beyond the locations where data are collected, also referred to as now-casting and to provide forecasting of weather conditions using NWP.

Thursday, November 5, 2015 - 11:00am - 12:00pm

J. William Helton (University of California, San Diego)

One of the main developments in optimization over the last 20 years is

Semi-Definite Programming. It treats problems which can be expressed as a

Linear Matrix Inequality (LMI). Any such problem is necessarily convex,

so the determining the scope and range of applicability comes down to the

question:

How much more restricted are LMIs than Convex Matrix Inequalities?

The talk gives a survey of what is known on this issue and will be

accessible to about anybody.

There are several main branches of this pursuit.

Semi-Definite Programming. It treats problems which can be expressed as a

Linear Matrix Inequality (LMI). Any such problem is necessarily convex,

so the determining the scope and range of applicability comes down to the

question:

How much more restricted are LMIs than Convex Matrix Inequalities?

The talk gives a survey of what is known on this issue and will be

accessible to about anybody.

There are several main branches of this pursuit.

Tuesday, November 18, 2008 - 2:00pm - 2:45pm

Annick Sartenaer (Facultés Universitaires Notre Dame de la Paix (Namur))

Joint work with Sven Leyffer (Argonne National Laboratory)

and Emilie Wanufelle (University of Namur).

Motivated by problems related to power systems analysis which give rise

to nonconvex mixed integer nonlinear programming (MINLP) problems,

we propose a global optimization method based on ideas and techniques

that can be easily extended to handle a large class of nonconvex MINLPs.

Our method decomposes the nonlinear functions appearing in the problem

to solve into one- and two-dimensional components for which piecewise

and Emilie Wanufelle (University of Namur).

Motivated by problems related to power systems analysis which give rise

to nonconvex mixed integer nonlinear programming (MINLP) problems,

we propose a global optimization method based on ideas and techniques

that can be easily extended to handle a large class of nonconvex MINLPs.

Our method decomposes the nonlinear functions appearing in the problem

to solve into one- and two-dimensional components for which piecewise

Thursday, October 30, 2008 - 10:00am - 10:50am

Tamara Kolda (Sandia National Laboratories)

Joint work with Evrim Acar, and Daniel M. Dunlavy

(Sandia National Laboratories).

(Sandia National Laboratories).

Monday, February 1, 2016 - 2:25pm - 3:25pm

Christian Grussler (Lund University)

We discuss optimal low-rank approximation of matrices with non-negative entries, without the need of a regularization parameter. It will be shown that the standard SVD-approximation can be recovered via convex-optimization, which is why adding mild convex constraints often gives an optimal solution. Moreover, the issue of computability will be addressed by solving our new convex problem via the so-called Douglas-Rachford algorithm. We will see that if there is a unique optimal solution than also the non-convex Douglas-Rachford will locally converge to it.