Workshop on Systems Biology, Thu, 08 Nov, 2007
Speaker: Paul Barton
Abstract
Recently, optimization problems with ordinary differential equations (ODEs) embedded have been applied to formulate and answer important questions in systems biology. These optimization problems are usually nonconvex and often exhibit multiple local minima, some of which are suboptimal. This talk will discuss the theory and implementation of finite, deterministic algorithms that can guarantee locating a global optimal solution of nonconvex optimization problems with ODEs embedded. In particular, we will discuss the theory required to construct convex relaxations of nonconvex functionals with ODEs embedded, and how the estimates generated by these relaxations can be computed practically.
Recent problem formulations have also created an increasing demand for algorithms capable of optimizing a dynamic system coupled with discrete decisions; these problems are termed Mixed-Integer Dynamic Optimization (MIDO). In the second half of this talk we will discuss how convex relaxations of nonconvex functionals with ODEs embedded can also be exploited to develop deterministic global optimization algorithms for MIDO.
URL: www.ricam.oeaw.ac.at/specsem/ssqbm/participants/abstracts/index.php
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