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The Akhiezer iteration and inverse-free solvers for Sylvester matrix equations
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Two inverse-free iterative methods are developed for solving Sylvester matrix equations when the spectra of the coefficient matrices are on, or near, known disjoint subintervals of the real axis. Both methods use the recently-introduced Akhiezer iteration: one to address an equivalent problem of approximating the matrix sign function applied to a block matrix and the other to directly approximate the inverse of the Sylvester operator. In each case this results in provable and computable geometric rates of convergence. When the right-hand side matrix is low rank, both methods require only low-rank matrix-matrix products. Relative to existing approaches, the methods presented here can be more efficient and require less storage when the coefficient matrices are dense or otherwise costly to invert. Applications include solving partial differential equations and computing Fr\'echet derivatives.
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Sign Embedding Quantum Algorithms for Matrix Equations and Matrix Functions
Sign-embedding quantum algorithms deliver explicit block-encodings for Sylvester equations and related matrix problems with query complexity linear in inverse-conditioning parameters and logarithmic in error tolerance.
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