One can conveniently describe a linear programming problem by stating the goal as minimizing / maximizing a function given a set of constraints. The word “linear” restricts the objective function and constraints to have a linear form. Despite this seemingly harsh restriction, LP can be used to attack a LOT of problems. This book is a Linear Programming 101 book and a book with visuals. I am a big fan of books with visuals.

The book is a story recounted by Turney Duff about his rise and fall on the Buy side. Turney Duff graduates from Ohio university in 1994 and starts hunting for a job on Wall Street. Thanks to his uncle Tucker and a random conversation about a soap opera with a HR lady, he gets a job at Morgan Stanley in Private Wealth group as a Sales assistant. Three years later, his dream of getting in to trading seems like a mirage until one day he gets a chance to organize a party.

[youtube https://www.youtube.com/watch?v=bXK-cEAOvac?rel=0]

There aren’t many Python books that talk about Data Structures. One of the reasons could be that Python provides a rich set of collections like list, dict, queue, set etc. that writing a new Data Structure might be unnecessary. That being the case, it is always helpful to know the time complexity of various operations in list, dict,etc. For some of the algorithms based on trees and graphs, there is no option but to build an abstract data structure to suit the problem one is trying to solve.

Mathematicians find it easier to understand and enjoy ideas which are clever rather than subtle. Measure theory is subtle rather than clever and so requires hard work to master. -- T. W. Körner