Author

Simge Küçükyavuz

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Preprint Oct 2026

Distributionally robust chance-constrained standard quadratic optimization with Wasserstein ambiguity

The standard quadratic optimization problem consists of minimizing a quadratic form over the standard simplex. It has highly relevant applications ranging from portfolio optimization to machine learning. However, if the quadratic form is neither convex nor concave, this problem is NP-hard. We study a distributionally r...

I. Bomze, Daniel de Vicente, Simge Küçükyavuz · 0 citations

Preprint Aug 2026

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators

This work develops the main components of CORe, including coordinate-wise optimality conditions, closed-form characterizations, and disjunctive reformulations, and demonstrates the framework across multiple problem families, including quadratic problems and robust single-index models.

Tong Xu, S. Fattahi, Andrés Gómez et al. · 0 citations

Preprint Aug 2026

SDDmiP.jl: A Software Package with a Provably Convergent Benders Algorithm for Multi-Stage Stochastic Mixed-Integer Programming

We present an open-source software package that implements a provably convergent Benders-type decomposition algorithm for multistage stochastic integer programs. In addition to standard cut families, such as Benders, strengthened Benders, and Lagrangian cuts, the algorithm incorporates rectified linear unit (ReLU) cuts...

Akul Bansal, Simge Küçükyavuz · 0 citations