convex optimization

A Storage-Optimal Convex Optimization Framework with Applications to Semidefinite Programming

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B9 L2 H2

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, there is a recent trend where heuristic approaches with unverifiable assumptions are overtaking more rigorous, conventional optimization methods at the expense of robustness. This trend can be overturned when we exploit dimensionality reduction at the core of optimization. I contend that even the classical convex optimization did not reach yet its limits of scalability.

Additive Schwarz Methods for Convex Optimization - Theory and Applications

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B5 L5 R5209

This talk is devoted to additive Schwarz methods for convex optimization. First, we propose an abstract framework for additive Schwarz methods for convex optimization. The framework's flexibility allows it to handle composite optimization problems and inexact local solvers. Moreover, it establishes a sharp convergence theory that agrees with the classical theory when addressing linear problems.

A new fast cooperative algorithm enhances target localization technique in wireless sensor networks

1 min read ·
Recently, a team of researchers from KAUST, including Yingquan Li, Bodhibrata Mukhopadhyay, and Mohamed-Slim Alouini, introduced a new cooperative localization technique that increases localization efficiency in wireless sensor networks. Known as FCUP (Fast Cooperative localization technique with Unknown transmit Power), this innovation contributes to tackling the longstanding challenge of localization without knowledge of transmitting power. FCUP offers an advanced solution to the problem of estimating the location and transmit power of nodes in real time. "FCUP represents a novel leap

Muhammad Hanif

Introduction Muhammad Hanif is a PhD Student at the University of Victoria, Victoria, Canada Education Muhammad got his B.S. and M.S. degrees in Electrical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan between 2005-2012. After graduation, he went to University of Victoria (UVic), Victoria, British Columbia, Canada as a Ph.D. in Electrical Engineering Research Interest Performance analysis of wireless systems Statistical signal processing Convex optimization Orthogonal frequency division multiplexing Synchronization in communication systems search

Khaled Alhujaili

Visiting Scholar, Information Science Lab
Exchange Student , Electrical Engineering at KAUST (King Abdullah University of Science and Technology)KAUST

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Aritra Dutta

Aritra Dutta is a Postdoctoral Research Fellow at the Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to that, he was a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Aritra Dutta received his bachelor degree in Mathematics (Hons.), Minor in Physics and Statistics from Presidency College in Calcutta University. He obtained a master degree in Mathematics and Computing from Indian