@misc{Sagnol, author = {Sagnol, Guillaume}, title = {A Class of Semidefinite Programs with rank-one solutions}, issn = {1438-0064}, doi = {10.1016/j.laa.2011.03.027}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-14933}, abstract = {We show that a class of semidefinite programs (SDP) admits a solution that is a positive semidefinite matrix of rank at most \$r\$, where \$r\$ is the rank of the matrix involved in the objective function of the SDP. The optimization problems of this class are semidefinite packing problems, which are the SDP analogs to vector packing problems. Of particular interest is the case in which our result guarantees the existence of a solution of rank one: we show that the computation of this solution actually reduces to a Second Order Cone Program (SOCP). We point out an application in statistics, in the optimal design of experiments.}, language = {en} } @misc{Sagnol, author = {Sagnol, Guillaume}, title = {Picos Documentation. Release 0.1.1.}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-17396}, abstract = {PICOS is a user friendly interface to several conic and integer programming solvers, very much like YALMIP under MATLAB. The main motivation for PICOS is to have the possibility to enter an optimization problem as a high level model, and to be able to solve it with several different solvers. Multidimensional and matrix variables are handled in a natural fashion, which makes it painless to formulate a SDP or a SOCP. This is very useful for educational purposes, and to quickly implement some models and test their validity on simple examples. Furthermore, with PICOS you can take advantage of the python programming language to read and write data, construct a list of constraints by using python list comprehensions, take slices of multidimensional variables, etc.}, language = {en} }