Notes
My notes (slides) during my PhD study in UMONS since Feb2017
My style : stepbystep, crystal clear, from first principle
Most of the things are selftaught : may have careless mistakes/typos. Email me if you catch one
Tutorial/Lecture material
Convex analysis, Operator Theory and Optimization paradigms
Iterative optimization algorithms and convergence / Lyapunov analysis
Second and higher order iterative optimisation algorithms
Nonconvex Optimizations
Nonnegative Matrix Factorizations : heuristics and theory
Linear Algebra / Matrix Theory
Projection operator
Tensor Algebra and Tensor methods
Fundamentals
Tensor Multilinear rank and rank
Why tensor : “easier” to get uniqueness of decomposition
Tensor method (3rd order method) for optimisation
Randomizations : Randomized Linear Algebra, Compressive Sensing, Random Matrix Theory
Paradigms in Machine Learning
