Because of yet another issue with my scholarship funding, I have decided to self-publish a new book, A Primer in Optimization, which is a tiny book that covers minimization and maximization theory using a bottom up approach. I’ve put this book as a bundle with previous book, The Hitchhiker’s Guide to Linear Models, which focuses on estimating linear models using R.

The two books are available as a 5 USD bundle on Leanpub.

A Primer in Optimization With a Focus on Microeconomics

This tiny book presents the theory behind Karush-Kuhn-Tucker (KKT) conditions and how to solve minimization and maximization problems for differentiable functions defined on Rⁿ subject to a finite number of equality and inequality constraints. I tried to explain KKT conditions and duality beyond just solving the Lagrangian. I worked on direct explanations about the Hyperplane Separation Theorem, Farkas’ Lemma, and the Envelope Theorem to connect mathematical notions to well-known micro results such as Roy’s Identity and Shepard’s Lemma for a straightforward interpretation of KKT multipliers as “shadow prices” and how changes in budgets and prices affect optimal decisions.

The goals are: * Cover what I consider are some gaps between Varian (1992) and/or Jehle (2011) compared to Mas-Colell (1995). * Provide a concise reference for students by presenting the results using consistent notation and terminology. * Provide a basis for more advanced topics in optimization, such as convex analysis and variational calculus.

The Hitchhiker’s Guide to Linear Models Based on the famous R programming language

This book aims to get straight to the point, and the only thing I assume here is that you have used spreadsheets at some point and that you are motivated to estimate linear models in R. Here I do not assume that you know how to install R or the basics of the R programming language.

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