High-Dimensional Optimization and Probability: With a View Towards Data Science (Hardcover, 2022)

Projection of a point onto a convex set via Charged Balls Method (E. Abbasov ).- Towards optimal sampling for learning sparse approximations in high dimensions (Adcock).- Recent Theoretical Advances in Non-Convex Optimization (Gasnikov).- Higher Order Embeddings for the Composition of the Harmonic Projection and Homotopy Operators (Ding).- Codifferentials and Quasidifferentials of the Expectation of Nonsmooth Random Integrands and Two-Stage Stochastic Programming (M.V. Dolgopolik).- On the Expected Extinction Time for the Adjoint Circuit Chains associated with a Random Walk with Jumps in Random Environments (Ganatsiou).- A statistical learning theory approach for the analysis of the trade-off between sample size and precision in truncated ordinary least squares (Raciti).- Recent theoretical advances in decentralized distributed convex optimization (Gasnikov).- On training set selection in spatial deep learning (M.T. Hendrix).- Surrogate-Based Reduced Dimension Global Optimization in Process Systems Engineering (Xiang Li).- A viscosity iterative method with alternated inertial terms for solving the split feasibility problem (Rassias).- Efficient Location-Based Tracking for IoT Devices Using Compressive Sensing and Machine Learning Techniques (Aboushelbaya).- Nonsmooth Mathematical Programs with Vanishing Constraints in Banach Spaces (Singh).

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