Uses the FORCE algorithm to solve the PECOK SDP.
gforce.PECOK(K, X = NULL, D = NULL, sigma_hat = NULL,
force_opts = NULL, X0 = NULL, E = NULL, gamma_par = FALSE)number of clusters.
\(n x d\) matrix. Either this or D must be specified.
\(d x d\) matrix. Either this or X must be specified.
\(d x d\) matrix. If D is specified, this argument should be the
estimated covariance matrix. It is not strictly necessary to provide it, but it should be for
optimal performance. If X is specified, it will be ignored.
tuning parameters. NULL signifies defaults will be used.
initial iterate. NULL signifies that it will be generated randomly from D_Kmeans. If supplied, E must be supplied as well.
strictly feasible solutions. NULL signifies that it will be generated randomly. If supplied, X0 must be supplied as well.
logical expression. If gamma_par==TRUE, then if \(\Gamma\) is computed,
a multi-threaded method is called, otherwise a single-threaded method is called.
C. Eisenach and H. Liu. Efficient, Certifiably Optimal High-Dimensional Clustering. arXiv:1806.00530, 2018.
J. Peng and Y. Wei. Approximating K-means-type Clustering via Semidefinite Programming. SIAM Journal on Optimization, 2007.
F. Bunea, C. Giraud, M. Royer and N. Verzelen. PECOK: a convex optimization approach to variable clustering. arXiv:1606.05100, 2016.