1,168 research outputs found
Randomized Extended Kaczmarz for Solving Least-Squares
We present a randomized iterative algorithm that exponentially converges in
expectation to the minimum Euclidean norm least squares solution of a given
linear system of equations. The expected number of arithmetic operations
required to obtain an estimate of given accuracy is proportional to the square
condition number of the system multiplied by the number of non-zeros entries of
the input matrix. The proposed algorithm is an extension of the randomized
Kaczmarz method that was analyzed by Strohmer and Vershynin.Comment: 19 Pages, 5 figures; code is available at
https://github.com/zouzias/RE
Fast linear algebra is stable
In an earlier paper, we showed that a large class of fast recursive matrix
multiplication algorithms is stable in a normwise sense, and that in fact if
multiplication of -by- matrices can be done by any algorithm in
operations for any , then it can be done
stably in operations for any . Here we extend
this result to show that essentially all standard linear algebra operations,
including LU decomposition, QR decomposition, linear equation solving, matrix
inversion, solving least squares problems, (generalized) eigenvalue problems
and the singular value decomposition can also be done stably (in a normwise
sense) in operations.Comment: 26 pages; final version; to appear in Numerische Mathemati
IPM-HLSP: An Efficient Interior-Point Method for Hierarchical Least-Squares Programs
Hierarchical least-squares programs with linear constraints (HLSP) are a type
of optimization problem very common in robotics. Each priority level contains
an objective in least-squares form which is subject to the linear constraints
of the higher priority hierarchy levels. Active-set methods (ASM) are a popular
choice for solving them. However, they can perform poorly in terms of
computational time if there are large changes of the active set. We therefore
propose a computationally efficient primal-dual interior-point method (IPM) for
HLSP's which is able to maintain constant numbers of solver iterations in these
situations. We base our IPM on the null-space method which requires only a
single decomposition per Newton iteration instead of two as it is the case for
other IPM solvers. After a priority level has converged we compose a set of
active constraints judging upon the dual and project lower priority levels into
their null-space. We show that the IPM-HLSP can be expressed in least-squares
form which avoids the formation of the quadratic Karush-Kuhn-Tucker (KKT)
Hessian. Due to our choice of the null-space basis the IPM-HLSP is as fast as
the state-of-the-art ASM-HLSP solver for equality only problems.Comment: 17 pages, 7 figure
Athena: A New Code for Astrophysical MHD
A new code for astrophysical magnetohydrodynamics (MHD) is described. The
code has been designed to be easily extensible for use with static and adaptive
mesh refinement. It combines higher-order Godunov methods with the constrained
transport (CT) technique to enforce the divergence-free constraint on the
magnetic field. Discretization is based on cell-centered volume-averages for
mass, momentum, and energy, and face-centered area-averages for the magnetic
field. Novel features of the algorithm include (1) a consistent framework for
computing the time- and edge-averaged electric fields used by CT to evolve the
magnetic field from the time- and area-averaged Godunov fluxes, (2) the
extension to MHD of spatial reconstruction schemes that involve a
dimensionally-split time advance, and (3) the extension to MHD of two different
dimensionally-unsplit integration methods. Implementation of the algorithm in
both C and Fortran95 is detailed, including strategies for parallelization
using domain decomposition. Results from a test suite which includes problems
in one-, two-, and three-dimensions for both hydrodynamics and MHD are given,
not only to demonstrate the fidelity of the algorithms, but also to enable
comparisons to other methods. The source code is freely available for download
on the web.Comment: 61 pages, 36 figures. accepted by ApJ
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