Search for: sparse-optimization
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    A Proximal Method for Composite Minimization

    , M.Sc. Thesis Sharif University of Technology Taherifard, Sara (Author) ; Mahdavi-Amiri, Nezamoddin (Supervisor) ; Soleimani-damaneh, Majid (Supervisor)
    We consider composite minimization problem of the form minx h(c(x)), where the function c : Rn ! Rm is smooth and the function h : Rm ! [+1;1] is usually convex or prox-regular, but may be nonsmooth. A wide variety of important optimization problems fall into this framework, and so far several studies have been done in this regard. One of these studies relates to the condition that the function h is finite convex and the algorithm uses a line search method. Another case is solving nonlinear programming problems using a penalty function where the function h is finite polyhedral. Research has also been done for the case where the function c is identity, that is c(x) = x.We describe an... 

    Context-Specific Reconstruction and Gap-Filling of Metabolic Networks by Sparse Reconciliation of Data Inconsistencies

    , M.Sc. Thesis Sharif University of Technology Fathi, Ali (Author) ; Tefagh, Mojtaba (Supervisor)
    With the increasingly collected biological data, appropriate usage of this data is of great importance for understanding and predicting biological systems and has been the aim of experiments and data collections. A famous category of biological data is known as “omics” which refers to transcriptomics, proteomics, metabolomics, and fluxomics, from different cells or tissues in various media and conditions. This set of data is regularly used for tasks such as studying cells and organisms, understanding cell states, cancer prediction, etc. and is of great importance in Systems Biology.In this thesis, we concentrate on studying cells or organisms using such data, where during that process, we... 

    Sparsity promotion in state feedback controller design

    , Article IEEE Transactions on Automatic Control ; Volume 62, Issue 8 , 2017 , Pages 4066-4072 ; 00189286 (ISSN) Babazadeh, M ; Nobakhti, A ; Sharif University of Technology
    A globally convergent algorithm for synthesis of sparse optimal state feedback (SOSF) control of linear time-invariant (LTI) systems is proposed. This problem is known to be NP-hard due to its combinatorial nature. A structured H2 norm controller design problem is intrinsically non-convex, even if a fixed structure is known in advance. The proposed algorithm minimizes the H2 norm performance index, simultaneously regularizes the sparsity of the control structure using the norm. It guarantees that the solution converges to a stationary point of the original problem. The algorithm is implemented using Linear Matrix Inequalities (LMIs) which are efficient, reliable and extendable to other...