**Soft-Thresholding and Max-Det Matrix Completion for Graphical Lasso.**In our ICML 2018 paper, we describe an algorithm that recovers the*graphical lasso*estimator of Friedman et al. (2008) in \(O(n^2/p + n)\) time over \(p\) parallel processors. Given the sample matrix \(\mathbf{X}=[\mathbf{x}_{1},\ldots,\mathbf{x}_{N}]_{i=1}^{N}\), we first compute the soft-thresholded sample covariance matrix \(C_{\lambda}\) in \(O(n^2/p)\) time and \(O(n\cdot N)\) memory. Then, we solve an associated maximum determinant matrix completion problem using \(C_{\lambda}\) as input in \(O(n)\) time and memory. [Associated paper].- Modified SeDuMi. A modified version of SeDuMi with identical syntax for
*large-and-sparse low-rank*semidefinite programs. If the \(n \times n\) solution matrix is low-rank and if the problem data is "sufficiently sparse", then each SeDuMi iteration costs \(O(n^{3})\) time and \(O(n^{2})\) memory. Lorentz cone is not (yet) supported. [Associated paper]. - fastlitz - Realistic litz wire characterization. Simulates litz wires in three-dimensions using the PEEC formulation. Computes the resistance of a wire, and the induced eddy loss when exposed to a magnetic field. Comes with a nice graphical user interface. [Associated paper] UPDATE 04/14: Removed dependencies on MATLAB Statistics Toolbox.

© 2018 Richard Y. Zhang.