This textbook presents a special solution of underdetermined linear
systems where the number of nonzero entries in the solution is very
small compared to the total number of entries. This is called
sparse solution. As underdetermined linear systems can be very
different, the authors explain how to compute a sparse solution by
many approaches. Sparse Solutions of Underdetermined Linear
Systems: Contains 72 algorithms for finding sparse solutions of
underdetermined linear systems and their applications for matrix
completion, graph clustering, and phase retrieval. Provides a
detailed explanation of these algorithms including derivations and
convergence analysis. Includes exercises for each chapter to help
the reader understand the material. This textbook is appropriate
for graduate students in math and applied math, computer science,
statistics, data science, and engineering. Advisors and postdocs
will also find the book of interest. It is appropriate for the
following courses: Advanced Numerical Analysis, Special Topics on
Numerical Analysis, Topics on Data Science, Topics on Numerical
Optimization, and Topics on Approximation Theory.
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