This award provides funding for theoretical and software development of novel algorithms for processing large-scale optimization models arising in Signal Processing, Medical Image Reconstruction, Machine Learning, and high-dimensional Statistical inference, where huge and steadily growing amounts of underlying data result in the necessity to process optimization problems with hundreds of thousands of variables and constraints. In addition, some of applications, such as low rank matrix approximations, lead to problems with difficult geometry, which amplifies significantly the challenges caused by sheer problem sizes. These challenges will be met via developing algorithms with cheap iterations utilizing state-of-the-art approaches, primarily bilinear saddle point reformulation of the problem of interest combined with duality-based handling difficult geometry and accelerating algorithms via various types of randomization. Theoretical and algorithmic developments will be adjusted to several generic applications (sparsity- and low-rank oriented Signal Processing and Machine Learning, extensions of total variation-based Image processing, and some others) and will be aimed at developing optimization techniques with good theoretical performance guarantees and visible practical potential; the latter will be validated by extensive numerical experimentation with both simulated and real life problems.
If successful, the research will advance theory and practice of optimization by enriching its abilities to process large-scale/complex geometry problems and thus will contribute significantly to the computational toolboxes in Signal Processing, Image Reconstruction, Machine Learning, and some other subject domains. As a byproduct, the research will contribute to recent tendency of bridging the corresponding research communities, with clear mutual benefits. In addition, the results of the research could form the base of new Ph.D.-level optimization courses.
