Several general-purpose deterministic global optimization algorithms have been developed for mixed-integer nonlinear optimization problems over the past two decades. Central to the efficiency of such methods is their ability to construct sharp convex relaxations. Current global solvers rely on factorable programming techniques to iteratively decompose nonconvex factorable functions, until each intermediate expression can be outer-approximated by a convex feasible set. While it is easy to automate, this factorable programming technique often leads to weak relaxations.
This work studies the problem of sequentially recovering a sparse vector x_t and a vector from a low-dimensional subspace l_t from knowledge of their sum m_t=x_t+l_t. If the primary goal is to recover the low-dimensional subspace where the l_t's lie, then the problem is one of online or recursive robust principal components analysis (PCA). An example of where such a problem might arise is in separating a sparse foreground and a slowly changing dense background in a surveillance video.
The 12th annual Texas Wireless Summit continues the tradition of providing a forum for industry leaders and academics to discuss emerging technologies and business models that will shape the industry over the upcoming two to three years. Co-hosted by the Austin Technology Incubator and The University of Texas at Austin’s Wireless Networking and Communications Group (WNCG), the Summit has direct access to cutting edge research and innovations from industry leaders, investors, academics, and startups.
IEEE GLOBECOM is one of two flagship conferences of the IEEE Communications Society (ComSoc), together with the IEEE ICC. Each year the conference hosts over 1,000 peer-reviewed technical papers and a cutting-edge industry program. The conference meets in North America and attracts roughly 2,000 leading scientists, researchers and industry practitioners from around the world. This year, Dr. Robert Heath and Dr.
Traditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize link spectral efficiencies and capacities of current and future communication networks.
A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a data-dependent function, all under the constraint of differential privacy. This problem is central to many privacy-preserving algorithms for statistics and machine learning.
Millimeter wave communication is coming to a wireless network near you. Because of the small antenna size and the need for array gain, array processing is important in millimeter wave communication systems. This presentation provides an overview of millimeter wave communication systems. Particular attention is paid to the ways that MIMO communication has played a role in the past and how it may play a role in the future.
This year, Professor Alexandros Dimakis with the WNCG will give the Plenary Talk at the Université de Bordeaux's Algebra, Codes and Networks Conference. For more information, visit the ACN 2014 website.