activity
20172022
most citedSupermodularity and valid inequalities for quadratic optimization with indicators

2 citations · 2 across the 7 of their papers we have counts for

collaborators

11 papers

cs.LG2022

State-driven Implicit Modeling for Sparsity and Robustness in Neural Networks

Alicia Y. Tsai, Juliette Decugis, Laurent El Ghaoui +1

Implicit models are a general class of learning models that forgo the hierarchical layer structure typical in neural networks and instead define the internal states based on an ``e…

eess.SY2021

Enhanced Modeling of Contingency Response in Security-constrained Optimal Power Flow

Tuncay Altun, Ramtin Madani, Alper Atamturk +2

This paper provides an enhanced modeling of the contingency response that collectively reflects high-fidelity physical and operational characteristics of power grids. Integrating a…

math.OC20202 cited

Supermodularity and valid inequalities for quadratic optimization with indicators

Alper Atamturk, Andres Gomez

We study the minimization of a rank-one quadratic with indicators and show that the underlying set function obtained by projecting out the continuous variables is supermodular. Alt…

math.OC2020

Penalized Semidefinite Programming for Quadratically-Constrained Quadratic Optimization

Ramtin Madani, Mohsen Kheirandishfard, Javad Lavaei +1

In this paper, we give a new penalized semidefinite programming approach for non-convex quadratically-constrained quadratic programs (QCQPs). We incorporate penalty terms into the…

math.OC2019

Submodular Function Minimization and Polarity

Alper Atamturk, Vishnu Narayanan

Using polarity, we give an outer polyhedral approximation for the epigraph of set functions. For a submodular function, we prove that the corresponding polar relaxation is exact; h…

stat.ML2019

Rank-one Convexification for Sparse Regression

Alper Atamturk, Andres Gomez

Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact model of sparse regression with…