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- Necdet Serhat Aybat, Donald Goldfarb, Lifeng Chen
- Math. Program.
- 2011

The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. The linearly constrained nuclear normâ€¦ (More)

- Necdet Serhat Aybat, Wotao Yin, Yin Zhang, Amit Chakraborty
- 2008 IEEE Conference on Computer Vision andâ€¦
- 2008

Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a very limited numberâ€¦ (More)

- Donald Goldfarb, Necdet Serhat Aybat, Katya Scheinberg
- Math. Program.
- 2013

We present in this paper alternating linearization algorithms based on an alternating direction augmented Lagrangian approach for minimizing the sum of two convex functions. Our basic methods requireâ€¦ (More)

Gaussian graphical models are of great interest in statisti cal learning. Because the conditional independencies between different nodes corre spond to zero entries in the inverse covariance matrixâ€¦ (More)

- Necdet Serhat Aybat, Lingzhou Xue, Hui Zou
- Neural Computation
- 2013

Chandrasekaran, Parrilo, and Willsky (2012) proposed a convex optimization problem for graphical model selection in the presence of unobserved variables. This convex optimization problem aims toâ€¦ (More)

- Donald Goldfarb, Necdet Serhat Aybat
- Foundations of Computational Mathematics
- 2011

The matrix rank minimization problem has applications in many fields such as system identification, optimal control, low-dimensional embedding, etc. As this problem is NP-hard in general, its convexâ€¦ (More)

- Conghui Tan, Necdet Serhat Aybat, Yu-Hong Dai, Yuqiu Qian
- NIPS
- 2016

One of the major issues in stochastic gradient descent (SGD) methods is how to choose an appropriate step size while running the algorithm. Since the traditional line search technique does not applyâ€¦ (More)

- Xiao Wang, Necdet Serhat Aybat, Donald Goldfarb, Wei Liu
- SIAM Journal on Optimization
- 2017

In this paper we study stochastic quasi-Newton methods for nonconvex stochastic optimization, where we assume that noisy information about the gradients of the objective function is available via aâ€¦ (More)

- Donald Goldfarb, Necdet Serhat Aybat
- SIAM Journal on Optimization
- 2012

Abstract. We present in this paper two different classes of general K-splitting algorithms for solving finite-dimensional convex optimization problems. Under the assumption that the function beingâ€¦ (More)

Consider the problem of minimizing the sum of a smooth convex function and a separable nonsmooth convex function subject to linear coupling constraints. Problems of this form arise in manyâ€¦ (More)