CodeWDMR.m computes the Weight Determination by Manifold Regularization kernel matrix (see e.g. Manifold-Constrained Regressors in System Identification Henrik Ohlsson, Jacob Roll and Lennart Ljung, 47th IEEE Conference on Decision and Control, Cancun, Mexico, December 2008). Code can be downloaded here.
stateson is a state estimation algorithm which uses sum-of-norms regularization to handle infrequently occurring process noise disturbances (jumps). Stateson can be seen as a convexification of the general likelihood ratio approach proposed by A. Willsky and H. Jones in "A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems," IEEE Transactions on Automatic Control, vol. 21, no. 1, pp. 108-112, Feb 1976. Code can be downloaded here (a code-package with examples will be available soon). The code uses CVX which can be downloaded from http://cvxr.com/cvx/.
SON Clustering is a convex clustering method using regularization. A simple Matlab script that illustrates SON clustering can be downloaded here. For better performance, one should consider adding a kernel in front of the regularization. The code uses CVX which can be downloaded from http://cvxr.com/cvx/.
PWASON is a convex system identification method developed for the identification of piecewise affine models. A simple Matlab script that illustrates SON clustering can be downloaded here. The code uses CVX which can be downloaded from http://cvxr.com/cvx/.
CPRL (Compressive Phase Retrieval via Lifting) is a sparse convex phase retrieval algoritm. CPRL extends the type of applications that compressive sensing can be applied to those where only output magnitudes can be observed. More information can be found in the technical report arXiv.org. A simple Matlab package that illustrates CPRL (CVX and ADMM implementation) can be downloaded here. The code uses CVX which can be downloaded from http://cvxr.com/cvx/.
SparsePoly: a Matlab toolbox for finding sparse solutions of polynomial systems SparsePoly is a Matlab implementation of the polynomial basis pursuit and greedy algorithms described in Finding sparse solutions of systems of polynomial equations via group-sparsity optimization. The toolbox can be downloaded from Fabien Lauer's homepage.
NLBP solves nonlinear equation systems (for both sparse and dense solutions) and thereby extends the type of applications that compressive sensing can be applied to those where there are nonlinear relationships between the measurements and the unknowns. For further on NLBP (Nonlinear Basis Pursuit) see the report: Nonlinear Basis Pursuit. An example and code for NLBP can be downloaded from here. The code uses CVX which can be downloaded from http://cvxr.com/cvx/.
Informationsansvarig: Henrik Ohlsson
Senast uppdaterad: 2013-12-07