log_cholesky_unvec

Compute the Log-Cholesky Inverse Vectorization

Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (<doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (<doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (<doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.

Nicolas Escobar

riemtan

Riemannian Metrics for Symmetric Positive Definite Matrices

Jaroslaw Harezlak

log_cholesky_unvec function

<dl><dt>sigma</dt>
<dd>A symmetric positive-definite matrix of class <code>dppMatrix</code></dd>
<dt>w</dt>
<dd>A numeric vector representing the vectorized tangent image</dd></dl>

Arguments

Compute the Log-Cholesky Inverse Vectorization — log_cholesky_unvec

<dl>

<dt>sigma</dt>
<dd>A symmetric positive-definite matrix of class <code>dppMatrix</code></dd>


<dt>w</dt>
<dd>A numeric vector representing the vectorized tangent image</dd>

</dl>

log_cholesky_unvec: Compute the Log-Cholesky Inverse Vectorization

Description

Usage

Value

Arguments