Notes

  • My notes (slides) during my PhD study in UMONS since Feb-2017

  • My style : step-by-step, crystal clear, from first principle

  • Most of the things are self-taught : 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

Non-convex Optimizations

Non-negative Matrix Factorizations : heuristics and theory

Linear Algebra / Matrix Theory

Projection operator

Tensor Algebra and Tensor methods

  • Fundamentals

  • Tensor Multi-linear 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

  • Continuation parameter tuning

  • Performance measures of algorithm that are “fair” and “meaningful”