Distributed algorithms

Wednesday, February 25, 2015 - 10:15am - 11:05am
Yingyu Liang (Princeton University)
Since large scale applications often involve data partitioned across different servers, there is an increasing interest in optimization in the distributed model. We study two distributed algorithms: distributed Frank-Wolfe and distributed PCA.
Friday, October 17, 2014 - 10:05am - 10:55am
Natasha Flyer (National Center for Atmospheric Research)
Discretizations of PDEs have traditionally relied on structured meshes. Requirements for geometric flexibility, both to conform to highly irregular geometries as in topographical and urban features as well as to achieve local refinement in critical areas, have led to an increased use of unstructured meshes, often in the form of polygonal-type elements. Parallel to this trend, radial basis function-generated finite differences (RBF-FD) is an altogether alternative novel approach that is mesh-free.
Tuesday, June 3, 2014 - 9:00am - 10:30am
Angelia Nedich (University of Illinois at Urbana-Champaign)
The advances in wired and wireless technology necessitated the development of theory, models and tools to cope with new challenges posed by large-scale optimization problems over networks. The classical optimization works under the premise that all problem data is available to some central entity. This premise does not apply to large networked systems where typically each agent (node) in the network has access to its private local information and has a local view of the network only.
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