most citedMachine learning approach to chance-constrained problems: An algorithm based on the stochastic gradient descent

8 citations · 9 across the 2 of their papers we have counts for

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2 papers

math.OC2019★ 8 cited

Machine learning approach to chance-constrained problems: An algorithm based on the stochastic gradient descent

Lukáš Adam, Martin Branda

We consider chance-constrained problems with discrete random distribution. We aim for problems with a large number of scenarios. We propose a novel method based on the stochastic g…

math.OC2017★ 1 cited

Convergence of a Scholtes-type Regularization Method for Cardinality-Constrained Optimization Problems with an Application in Sparse Robust Portfolio Optimization

Martin Branda, Max Bucher, Michal Červinka +1

We consider general nonlinear programming problems with cardinality constraints. By relaxing the binary variables which appear in the natural mixed-integer programming formulation,…