The aim of the notes you'll find here is to suggest a direct, constructive path to results I find useful, beautiful, or simply interesting. This stems mainly from my experience reading papers where the presentation sometimes trades clarity for generality.

The emphasis is on intuition rather than rigour though I try to indicate where simplifications are made and what they may imply. Often, these simplifications allow for straightforward demonstrations. The references to more technical work can then be helpful to learn the results in full.

**Target audience**: advanced undergrads or grads in quantitative fields such as applied-maths, stats, comp-sci, etc, assuming a decent background in basic maths (in particular linear algebra, real analysis and probability theory). When the level of the notes is judged (arbitrarily) to be a bit more advanced, it is marked by a "⭒" symbol.

**Errors & Comments**: if you find anything dubious in the notes, please send me an email, feedback is always much appreciated.

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**kernel methods**(*assumes good knowledge of stats and real analysis*)(⭒) RKHS embeddings part 1: notes from a 2015 "kernel methods reading group" introducing

*reproducing kernel Hilbert space*embeddings of distributions and their use.(⭒) RKHS embeddings part 2: rest of the notes where we discuss probabilistic reasoning with kernel embeddings.

**convex optimisation**(*assumes familiarity with convexity*):introduction: introduction of the general minimisation problem and hint at generic iterative methods.

convex analysis part 1: the

*subdifferential*and the*first-order optimality condition*.(⭒) convex analysis part 2: the

*convex conjugate*along with some useful properties such as*Fenchel's inequality*and the*Fenchel-Moreau*theorem.(⭒) convex analysis part 3:

*strict*and*strong*convexity, the*Bregman divergence*and link between*lipschitz continuity*and*strong convexity*.projected gradient descent:

*normal cone*,*Euclidean projection*and*projected gradient descent*.mirror descent algorithm:

*generalised projected gradient descent*and the*mirror descent algorithm*.thoughts on first order methods:

*first order methods*,*minimising sequence*,*admissible direction*,*generalised projected gradient descent*(again).(⭒) splitting methods and ADMM:

*splitting methods*in optimisation,*proximal methods*and*ADMM*.

**Matrix theory**:matrix inversion lemmas: re-obtaining the

*Woodbury formula*and the*Sherman-Morrison formula*(with some code).

© T. Lienart. Last modified: January 17, 2019. Website built with JuDoc.jl.