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20172026
most citedEfficient Learning of Distributed Linear-Quadratic Controllers

8 citations · 19 across the 20 of their papers we have counts for

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12 papers · 1 filter

math.OC2026

Convexification of mixed-integer quadratic optimization via decision diagrams

Soobin Choi, Salar Fattahi, Andres Gomez +2

We study mixed-integer quadratic optimization (MIQO) problems with indicator variables. We propose a unified framework, based on decision diagrams, that serves both to solve the as…

math.OC2026

On the Absence of Identifiable Manifolds in Finite-Max Composite Optimization

Yifan Wang, Jianhao Ma, Salar Fattahi

In nonsmooth optimization, identifiable sets describe the local region eventually reached by sequences converging to a prescribed critical point. When such a set is a manifol…

math.OC2026

Oracle-Based Distributionally Robust Optimization under Optimal Transport Ambiguity Sets

Guixian Chen, Salar Fattahi, Soroosh Shafiee

Distributionally robust optimization (DRO) with optimal transport ambiguity sets is traditionally solved by reformulating the minimax problem into a single-level convex program. Wh…

math.OC2026

Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators

Tong Xu, Salar Fattahi, Andrés Gómez +1

We consider mixed-integer convex optimization problems in which binary indicators control continuous variables. We introduce the \emph{Coordinate Optimality Reformulation} (CORe) f…

math.OC2026

Solving Convex Quadratic Optimization with Indicators Over Structured Graphs

Aaresh Bhathena, Salar Fattahi, Andrés Gómez +1

This paper studies convex quadratic minimization problems in which each continuous variable is coupled with a binary indicator variable. We focus on the structured setting where th…

math.OC20255 cited

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization

Gavin Zhang, Salar Fattahi, Richard Y. Zhang

In practical instances of nonconvex matrix factorization, the rank of the true solution is often unknown, so the rank of the model can be overspecified as $r>r^{\st…