2024/10/28 by Aleksandros Sobczyk, Sobczyk, Aleksandros · 1 voice · 3 citations
Computer Science · Mathematics · #65F15 #Data Structures and Algorithms (cs.DS) #F.2.1 #FOS: Computer and information sciences #FOS: Mathematics #Numerical Analysis (math.NA) #cs.DS #math.NA
paper · pdf · doi:10.48550/arxiv.2410.21550
arxiv published 2024/10/28 · arxiv updated 2025/04/27
In this work we revisit the arithmetic and bit complexity of Hermitian eigenproblems. Recently, [BGVKS, FOCS 2020] proved that a (non-Hermitian) matrix can be diagonalized with a randomized algorithm in O(nωlog2(n/ε)) arithmetic operations, where ω\lesssim 2.371 is the square matrix multiplication exponent, and [Shah, SODA 2025] significantly improved the bit complexity for the Hermitian case. Our main goal is to obtain similar deterministic complexity bounds for various Hermitian eigenproblems. In the Real RAM model, we show that a Hermitian matrix can be diagonalized deterministically in O(nωlog(n)+n2polylog(n/ε)) arithmetic operations, improving the classic deterministic \widetilde O(n3) algorithms, and derandomizing the aforementioned state-of-the-art. The main technical step is a complete, detailed analysis of a well-known divide-and-conquer tridiagonal eigensolver of Gu and Eisenstat [GE95], when accelerated with the Fast Multipole Method, asserting that it can accurately diagonalize a symmetric tridiagonal matrix in nearly-O(n2) operations. In finite precision, we show that an algorithm by Schönhage [Sch72] to reduce a Hermitian matrix to tridiagonal form is stable in the floating point model, using O(log(n/ε)) bits of precision. This leads to a deterministic algorithm to compute all the eigenvalues of a Hermitian matrix in O(nωF(log(n/ε))+n2polylog(n/ε)) bit operations, where F(b)∈\widetilde O(b) is the bit complexity of a single floating point operation on b bits. This improves the best known \widetildeO(n3) deterministic and O(nωlog2(n/ε)F(log(n/ε))) randomized complexities. We conclude with some other useful subroutines and with open problems.