Prof. Dean Doron

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On Approximating the Eigenvalues of Stochastic Matrices in Probabilistic Logspace

Dean Doron, Amir Sarid, Amnon Ta-Shma

We show that approximating the second eigenvalue of stochastic operators is BPL-complete, thus giving a natural problem complete for this class. We also show that approximating any eigenvalue of a stochastic and Hermitian operator with constant accuracy can be done in BPL. This work together with related work on the subject reveal a picture where the various space-bounded classes (e.g., probabilistic logspace, quantum logspace and the class DET) can be characterized by algebraic problems (such as approximating the spectral gap) where, roughly speaking, the difference between the classes lies in the kind of operators they can handle (e.g., stochastic, Hermitian or arbitrary).

Publication language English
Pages 393-420
Journal Computational Complexity
Volume 26
Issue number 2
Publication status Published - 01.06.2017

Keywords

Bounded space computation
Complete problems
Random walks
Randomized algorithms

ASJC Scopus subject areas

Theoretical Computer Science
General Mathematics
Computational Theory and Mathematics
Computational Mathematics
Access to Document
10.1007/s00037-016-0150-y
Other files and links
Link to publication in Scopus