Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms
نویسندگان
چکیده
منابع مشابه
Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms
We develop and analyze a broad family of stochastic/randomized algorithms for inverting a matrix. We also develop specialized variants maintaining symmetry or positive definiteness of the iterates. All methods in the family converge globally and linearly (i.e., the error decays exponentially), with explicit rates. In special cases, we obtain stochastic block variants of several quasiNewton upda...
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ژورنال
عنوان ژورنال: SIAM Journal on Matrix Analysis and Applications
سال: 2017
ISSN: 0895-4798,1095-7162
DOI: 10.1137/16m1062053