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Course description |
Optimization -- finding maxima or minima of functions -- is
necessary in most cases when a problem has been "solved on paper" or
just formulated, but we need to follow with computing the value of its
solution. Most practically interesting problems and many theoretical
ones do not have analytical solutions. Therefore, optimization in the
technical sense deals with the algorithms for numerically finding
extrema of functions and with finding conditions when these extrema
exist. In this course we will focus on continuous optimization
(that is, over continuous multidimensional domains) and, within this
area, on convex optimization. We will look at applications in
statistics (e.g estimating parameters by maximizing likelihood) and in
related machine learning methods (support vector machines,
boosting). Convexity is intimately related to a class of statistical
models called exponential family models and to the information
theoretic concepts of entropy and Kullback-Liebler divergence;
our course will also examine these from a computational point of view.
Below is a list of topics that we are likely to cover (the ordering
being logical, rather than chronological)
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Contact the instructor at: mmp@stat.washington.edu
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