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QuasiTriangular

A Julia package for quasi upper triangular matrices strongly inspired by https://github.com/JuliaLang/julia/blob/master/stdlib/LinearAlgebra/src/triangular.jl

WORK IN PROGRESS

Type QuasiUpperTriangular stores a quasi upper triangular matrix in a square matrix. The various algorithms ignore the lower zero elements

Functions

Conventional mul! functions are defined to allow normal multiplication using *

lets define $Q$ as a QuasiUpperTriangular matrix.

Matrix - vector product

  • $Q * \vec{v}$
  • $Q^T * \vec{v}$

Matrix - Matrix product

  • $Q * {A}$
  • $Q^T * A$
  • $A * Q$
  • $A * Q^T$

Linear problem solvers

  • ldiv!(Q, A) solves $Q*X = A$, Solves by back substitution. Lower off-diagonal elements make 2 * 2 problems that are solved explicitly.
  • rdiv!(A, Q) solves $X*Q = A$, Solves by back substitution. Lower off-diagonal elements make 2 * 2 problems that are solved explicitly.
  • rdiv!(A, Q') solves $X*Q^T = A$, Solves by back substitution. Lower off-diagonal elements make 2 * 2 problems that are solved explicitly.

lets define $r$ and $s$ as floats

Note: these functions break conventions and mutate their last argument

  • I_plus_rA_ldiv_B!(r, Q, b) solves $(I + rQ)*\vec{x} = \vec{b}$
  • I_plus_rA_ldiv_B!(r, Q, B) solves $(I + rQ)*X = B$
  • I_plus_rA_plus_sB_ldiv_C!(r, s, Q1, Q2, c) solves $(I + rQ_1 + sQ_2)*\vec{x} = \vec{c}$
  • I_plus_rA_plus_sB_ldiv_C!(r, s, Q1, Q2, C) solves $(I + rQ_1 + sQ_2)*X = C$

TODO

  • assert that sub-diagonal does not contain consecutive non-zero elements
  • handle quasi lower triangular matrices
  • profile, benchmark, and reintroduce BLAS based implementations if needed (for specific strided-matrix element-types)