A highly regarded method to obtain an orthonormal basis, $Z$, for the null space of a matrix $A^{T}$ is the $QR$ decomposition of $A$, where $Q$ is the product of Householder matrices. In several optimization contexts $A(x)$ varies continuously with $x$ and it is desirable the $Z(x)$ vary continuously also. In this note we demonstrate that the standard implementation of the $QR$ decomposition does not yield an orthonormal basis $Z(x)$ whose elements vary continuously with $x$. We suggest three possible remedies
The Null Space Problem is that of finding a sparsest basis for the null space (null basis) of a $t ...
The sparse null space basis problem is the following: $A t \times n$ matrix $A (t less than n)$ is ...
AbstractSuppose that {ek} is an orthonormal basis for a separable, infinite-dimensional Hilbert spac...
AbstractIn this paper, we propose a new method to efficiently compute a representation of an orthogo...
AbstractMany algorithms for solving eigenproblems need to compute an orthonormal basis. The computat...
AbstractLet Δ ϵ Cnxn be an Hermitian positive definite matrix. We propose algorithms for the numeric...
. In this paper we consider smooth orthonormal decompositions of smooth time varying matrices. Among...
In this report we review the algorithms for the QR decomposition that are based on the Schmidt ortho...
We present algorithms for computing a sparse basis for the null space of a sparse underdetermined m...
A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of...
In 1971, Householder and Fox [26] introduced a method for computing an orthonormal basis for the ran...
A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of...
Assume H is a Hilbert space and K is a dense linear (not necessarily closed) subspace. The question ...
We revisit the numerical stability of the two-level orthogonal Arnoldi (TOAR) method for computing a...
Abstract. The symmetric orthogonalization, which is obtained from the polar decomposition of a matri...
The Null Space Problem is that of finding a sparsest basis for the null space (null basis) of a $t ...
The sparse null space basis problem is the following: $A t \times n$ matrix $A (t less than n)$ is ...
AbstractSuppose that {ek} is an orthonormal basis for a separable, infinite-dimensional Hilbert spac...
AbstractIn this paper, we propose a new method to efficiently compute a representation of an orthogo...
AbstractMany algorithms for solving eigenproblems need to compute an orthonormal basis. The computat...
AbstractLet Δ ϵ Cnxn be an Hermitian positive definite matrix. We propose algorithms for the numeric...
. In this paper we consider smooth orthonormal decompositions of smooth time varying matrices. Among...
In this report we review the algorithms for the QR decomposition that are based on the Schmidt ortho...
We present algorithms for computing a sparse basis for the null space of a sparse underdetermined m...
A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of...
In 1971, Householder and Fox [26] introduced a method for computing an orthonormal basis for the ran...
A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of...
Assume H is a Hilbert space and K is a dense linear (not necessarily closed) subspace. The question ...
We revisit the numerical stability of the two-level orthogonal Arnoldi (TOAR) method for computing a...
Abstract. The symmetric orthogonalization, which is obtained from the polar decomposition of a matri...
The Null Space Problem is that of finding a sparsest basis for the null space (null basis) of a $t ...
The sparse null space basis problem is the following: $A t \times n$ matrix $A (t less than n)$ is ...
AbstractSuppose that {ek} is an orthonormal basis for a separable, infinite-dimensional Hilbert spac...