A General Framework for a Class of Primal-Dual Algorithms for TV Minimization

Speaker: 

John Esser

Institution: 

UCLA Department of Mathematics

Time: 

Monday, September 28, 2009 - 4:00pm

Location: 

RH 306

In this talk, based on joint work with Xiaoqun Zhang and Tony
Chan, I will discuss some generalizations and extensions of the
primal-dual hybrid gradient (PDHG) algorithm proposed by Zhu and Chan. The
PDHG method applied to a saddle point formulation of a convex minimization
problem proceeds by alternating proximal steps that maximize and minimize
penalized forms of the saddle function. This can be useful for producing
explicit algorithms for large non-differentiable convex problems, and a
slight modification to the method can be made to guarantee convergence.
I will mainly focus on the connections to related algorithms including
proximal forward backward splitting, split Bregman and split inexact Uzawa
methods. For the problem of minimizing sums of convex functionals
composed with linear operators, I will show how to use operator splitting
techniques that allow the modified PDHG method to be effectively applied.
Specific applications to constrained TV deblurring and compressive sensing
problems will be presented.

Subspace Techniques for Nonlinear Optimization

Speaker: 

Ya-xiang Yuan

Institution: 

Institute of Computational Mathematics and Scientific/Eng. Computing, Chinese Academy of Sciences

Time: 

Tuesday, October 13, 2009 - 11:00am

Location: 

RH 440 R

We review various subspace techniques that are used in constructing of numerical methods for nonlinear optimization. The subspace techniques are getting more and more important as the optimization problems we have to solve are getting larger and larger in scale. The applications of subspace techniques have the advantage of reducing both computation cost and memory size. Actually in many standard optimization methods (such as conjugate gradient method, limited memory quasi-Newton method, projected gradient method, and null space method) there are ideas or techniques that can be viewed as subspace techniques. For constrained optimization, by using subspace approach, we can have a better understanding of some numerical methods, such as the null space method.

The essential part of a subspace method is how to choose the subspace in which the trial step or the trust region should belong. Model subspace algorithms for unconstrained optimization and constrained optimization will be discussed respectively. We will also consider subspace techniques for solving nonlinear equations and nonlinear least squares.

Acoustic Wave Driven Microfluidic Biochips

Speaker: 

Ronald Hoppe

Institution: 

University of Houston, University of Augsburg

Time: 

Monday, October 26, 2009 - 4:00pm

Location: 

RH 306

Biochips are physically and/or electronically controllable miniaturized labs. They are used for combinatorial chemical and biological analysis in environmental and medical studies, e.g., for high throughput screening, hybridization and sequencing in genomics, protein profiling in proteomics, and cytometry in cell analysis. The precise positioning of the samples (e.g., DNA or proteins) on the surface of the chip in picoliter to nanoliter volumes can be done either by means of external forces (active devices) or by specific geometric patterns (passive devices). The active devices which will be considered here are microfluidic biochips where the core of the technology are nanopumps featuring surface acoustic waves generated by electric pulses of high frequency. These waves propagate like a miniaturized earthquake (nanoscale earthquake), enter the fluid filled channels on top of the chip and cause an acoustic streaming in the fluid which provides the transport of the samples. The mathematical model represents a multiphysics problem consisting of the piezoelectric equations coupled with multiscale compressible Navier-Stokes equations that have to be treated by an appropriate homogenization. We discuss the modeling approach, present algorithmic tools for the numerical simulation and address optimal design issues as well. In particular, the optimal design of specific parts of the biochips leads to large-scale optimization problems. In order to reduce the computational complexity, we present a combination of domain decomposition and balanced truncation model reduction which allows explicit error bounds for the error between the reduced order and the fine-scale optimization problem. It is shown that this approach gives rise to a significant reduction of the problem size while maintaining the accuracy of the approximation. The results are based on joint work with Harbir Antil, Roland Glowinski, Matthias Heinkenschloss, Daniel Koster, Christopher Linsenmann, Kunibert Siebert, Danny Sorensen, Tsorng-Whay Pan, and Achim Wixforth.

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