Introduction to Nonsmooth Analysis and Optimization

·
· SIAM
Ebook
461
Pages
Eligible

About this ebook

Functions that are not differentiable in the classical sense have become a central tool in modern mathematical models for imaging, inverse problems, machine learning, and optimal control of differential equations. These models are increasingly formulated in infinite-dimensional function spaces to be independent of problem size and discretization quality. This book presents a unified and rigorous introduction to the infinite-dimensional analysis and algorithmic solution of nonsmooth optimization problems arising from the above-mentioned models, from the necessary theoretical tools of nonsmooth analysis to state-of-the-art algorithms and their convergence and stability analysis.

Introduction to Nonsmooth Analysis and Optimization offers a thorough examination of analysis and algorithms—first- and second-order methods—in infinite dimensions, as well as a self-contained and accessible introduction to set-valued and variational analysis for optimization problems (including novel calculus results for relevant situations). It is accompanied by Julia code to replicate the numerical results.

This book is intended for advanced undergraduate and graduate students with a solid background in calculus and linear algebra. Prior knowledge of functional analysis and nonlinear optimization is helpful but not assumed. Researchers in data science and machine learning who have an interest in the mathematical foundations of modern nonsmooth optimization algorithms will also find the book useful.

This book is appropriate for courses in convex analysis, nonsmooth optimization, and set-valued/variational analysis.

Functions that are not differentiable in the classical sense have become a central tool in modern mathematical models for imaging, inverse problems, machine learning, and optimal control of differential equations. These models are increasingly formulated in infinite-dimensional function spaces to...

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About the author

Christian Clason is professor of mathematical optimization in the Department of Mathematics and Scientific Computing at the University of Graz. His research areas are nonsmooth and PDE-constrained optimization, inverse problems, and applications to biomedical imaging.

Tuomo Valkonen is a principal scientist at the MODEMAT Research Center in Mathematical Modeling and Optimization. His research areas cover nonsmooth optimization and analysis, bilevel optimization, inverse problems, and relevant aspects of measures and their geometry.

Christian Clason is professor of mathematical optimization in the Department of Mathematics and Scientific Computing at the University of Graz. His research areas are nonsmooth and PDE-constrained optimization, inverse problems, and applications to biomedical imaging. Tuomo Valkonen is a principa...

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