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Summary

This chapter introduces matrix factorizations – somewhat like the reverse of matrix multiplication. It starts with the eigendecomposition of symmetric matrices, then generalizes to normal and asymmetric matrices. It introduces the basics of the singular value decomposition (SVD) of general matrices. It discusses a simple application of the SVD that uses the largest singular value of a matrix (the spectral norm), posed as an optimization problem, and then describes optimization problems related to eigenvalues and the smallest singular value. (The “real” SVD applications appear in subsequent chapters.) It discusses the special situations when one can relate the eigendecomposition and an SVD of a matrix, leading to the special class of positive (semi)definite matrices. Along the way there are quite a few small eigendecomposition and SVD examples.

We present below a new series of conjectures and openproblems in the fields of (global) Optimization and Matrix analysis, in thesame spirit as our recently published paper [J.-B. Hiriart-Urruty, Potpourri of conjectures and open questions in Nonlinear analysis and Optimization. SIAMReview49 (2007) 255–273]. With each problem come a succinct presentation, a list of specificreferences, and a view on the state of the art of the subject.

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3 - Matrix Factorization: Eigendecomposition and SVD

Summary

A new series of conjectures and open questionsin optimization and matrix analysis

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2 results

3 - Matrix Factorization: Eigendecomposition and SVD

Summary

A new series of conjectures and open questionsin optimization and matrix analysis