• Notes (in form of slides) written by me during my PhD study in UMONS since Feb 2017

  • I am a slow learner, I write it step-by-step from the first principle in a crystal clear manner, no matter how simple the thing is

  • All these things are self-taught : there may be careless mistakes/typos. Email me if you catch one

  • This page require MathJax to read

Tutorial/Lecture material

First order iterative optimization algorithms and related convergence / Lyapunov analysis

Optimization paradigms

Non-convex Optimizations

Non-negative Matrix Factorizations : theoretical and heuristics

Determinant and log determinant of matrices

Projection operator

Randomized Methods : Randomized Linear Algebra, Compressive Sensing, Random Matrix Theory

Tensor Algebra and Tensor methods

  • Fundamentals

  • Tensor Multi-linear rank and rank

  • Why tensor : “easier” to get uniqueness of decomposition