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Life sciences · Preprint
arXiv · September 10, 2026
The material analysed did not support any firm read.
This is a preprint in computational finance and optimization, not medical or clinical science. It presents a branch-and-bound algorithm for robust sparse portfolio selection under uncertainty. It has no direct bearing on clinical practice, biomedical research, or patient outcomes.
Preprint.
This is a preprint in computational finance and optimization, not medical or clinical science. It presents a branch-and-bound algorithm for robust sparse portfolio selection under uncertainty. It has no direct bearing on clinical practice, biomedical research, or patient outcomes.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a mathematical optimization paper with no clinical, biological, or medical content; it is outside the scope of PeerCurrent's evidence base for clinicians and life-science professionals.
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We investigate mean-variance portfolio selection with an $\ell_0$-penalty to promote sparsity in asset allocations. Uncertainty in the mean return vector is incorporated through an ellipsoidal uncertainty set, yielding a robust sparse optimization framework. We characterize the structure of both local and global minimizers and exploit these properties in the risk minimization and return maximization formulations. Building on this structural insight, we develop a branch-and-bound algorithm tailored to the resulting robust sparse portfolio problems, together with a new pruning rule that can discard exponentially many candidate portfolios in a single step. Extensive computational experiments on real market data, together with comparisons against a mixed-integer second-order cone programming solver, demonstrate the effectiveness and competitiveness of the proposed approach.
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