Browsing by Subject "L0-norm"

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    Minimizers of sparsity regularized robust loss functions
    (2021-06) Akkaya, Deniz
    We study the structure of the local and global minimizers of the Huber loss and the sum of absolute deviations functions regularized with a sparsity penalty L0 norm term. We char-acterize local minimizers for both loss functions, and establish conditions that are necessary and sufficient for local minimizers to be strict. A necessary condition is established for global minimizers, as well as non-emptiness of the set of global minimizers. The sparsity of minimizers is also studied by giving bounds on a regularization parameter controlling sparsity. Results are illustrated in numerical examples.
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    Sparsity penalized mean–variance portfolio selection: analysis and computation
    (SPRINGER HEIDELBERG, 2024-11-25) Şen, Buse; Akkaya, Deniz; Pınar, Mustafa Çelebi
    We consider the problem of mean–variance portfolio selection regularized with an -penalty term to control the sparsity of the portfolio. We analyze the structure of local and global minimizers and use our results in the design of a Branch-and-Bound algorithm coupled with an advanced start heuristic. Extensive computational results with real data as well as comparisons with an off-the-shelf and state-of-the-art (MIQP) solver are reported.