Bigmath Advanced Course 4: Large scale and distributed optimization

The goal of the training course, realized within the H2020 Marie Skłodowska-Curie project Big Data Challenges for Mathematics, Grant Agreement Nbigmath_logo2o 812912,  is to provide an overview of tools and algorithms in the area of large scale and distributed optimization. An illustrative examples which help in understanding how optimization-based modelling can be applied on  machine learning problems will be part of the course. The topics covered by the course are the following: Machine learning and optimization; Optimality conditions for unconstrained problems; Convexity; Line search methods; Gradient methods; Second order methods; Optimality conditions for constrained problems; Augmented Lagrangian methods; Parallel methods: duality theory and dual subgradient method; primal decomposition; dual decomposition; augmented Lagrangian; alternating direction method of multipliers. Distributed methods: distributed gradient descent; stochastic distributed methods. Part of the course will be devoted to computer labs for software/implementation tutorial. The course is carried out in 4 days, with 6 hours of lectures each day.

The morning lectures will be livestreamed while the afternoon sessions will be devoted to practical tutorials.

Venue: University of Novi Sad, Main Recorate Building, room 1/9, Dr. Zorana Djindjica Street No. 1, 21000 Novi Sad, Serbia

Time schedule:

January 27, 9.30-13, 14-17

January 28, 9.30-13, 14-17

January 30, 9.30-13, 14-17

January 31, 9.30-13.14-17

The course is opened for everybody, if you plan to attend please contact Natasa Krejic at

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