# Current Stack of Interesting Literature

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**Amir Beck's**excellent text:*First-Order Methods In Optimization*. A really well-written text bringing together a wealth of material on fundamental optimization theory and first order convex optimization algorithms**Nemirovski's**recent update to his classical,*Lectures on Modern Convex Optimization*. It's first three chapters are the natural next step after internalizing the*MOSEK modeling cookbook*for understanding conic optimization!*An introduction to optimization on smooth manifolds*: The title! Written by Nicolas Boumal (current maintaner of PyManOPT!)*Computational Optimal Transport*: A reall well-written introduction to Optimal Transport for those with a solid mathematical background (co-authored by none other than*Marco Cuturi*!)- Semidefinite approximations of the matrix logarithm: Find (an evolving) commentary on the paper
**HERE** - Distributional Reinforcement Learning with Quantile Regression — for a comprehensive introduction to distributional RL see HERE
- Practical Near Neighbour Search via Group testing — really cool work that uses the notion of Distance Sensitive Bloom Filters coupled with ideas from Group Testing to devise a really efficient framework for the approximate nearest neighbor search problem (they outperform FAISS by multiple factors!)
- Numerical Linear Algebra,
*Lloyd N. Trefethen and David Bau III*: Need to iron out my understanding of NLA, and this classical text is the best there is for a (relatively) self-contained course! Can then graduate to using*Matrix Computations*as a reference for all my LA needs like every sane applied mathematician XD. - Introduction to Online Convex Optimization: Elad Hazan's text on OCO. Online learning is really cool, and I wanna learn about it!
- Convex Optimization: Algorithms and Complexity: A beautiful monograph on the algorithmics of Convex optimization, plan on reading in conjunction with Boyd's theory portion.
- Non-Convex optimization for Machine Learning: Prateek Jain's monograph on some broad ideas in non-convex optimization, really exciting stuff!
- Tengyu Ma's StatML notes: Already have some background in learning theory from Shai's excellent text — plan to use Ma's notes as a reference while going through the above stuff, his notes also have some material on NTK ideas!
- A User's guide to Measure Theoretic Probability Theory,
*David Pollard*: An amazing text giving a self-contained tour of MTPT (ought to probably be sufficient for wannabe applied mathematician like me XD).