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Annotated Algorithms in Python



Download free course Annotated Algorithms in Python, pdf file on 388 pages by by Massimo Di Pierro.
This book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. The lectures cover multiple classes, including Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. These lectures teach the core knowledge required by any scientist interested in numerical algorithms and by students interested in computational finance.

The algorithms you will learn can be applied to different disciplines. Throughout history, it is not uncommon that an algorithm invented by a physicist would find application in, for example, biology or finance.

Table of contents

  • Introduction
  • Main Ideas
  • About Python
  • Book Structure
  • Book Software
  • Overview of the Python Language
  • About Python
  • Types of variables
  • Python control flow statements
  • Classes
  • File input/output
  • How to import modules
  • Theory of Algorithms
  • Order of growth of algorithms
  • Recurrence relations
  • Types of algorithms
  • Timing algorithms
  • Data structures
  • Tree algorithms
  • Graph algorithms
  • Greedy algorithms
  • Artificial intelligence and machine learning
  • Long and infinite loops
  • Numerical Algorithms
  • Well-posed and stable problems
  • Approximations and error analysis
  • Standard strategies
  • Linear algebra
  • Sparse matrix inversion
  • Solvers for nonlinear equations
  • Optimization in one dimension
  • Functions of many variables
  • Nonlinear fitting
  • Integration
  • Fourier transforms
  • Differential equations
  • Probability and Statistics
  • Probability
  • Combinatorics and discrete random variables
  • Random Numbers and Distributions
  • Randomness, determinism, chaos and order
  • Real randomness
  • Entropy generators
  • Pseudo-randomness
  • Parallel generators and independent sequences
  • Generating random numbers from a given distribution
  • Probability distributions for continuous random variables
  • Resampling
  • Binning
  • Monte Carlo Simulations
  • Introduction
  • Error analysis and the bootstrap method
  • A general purpose Monte Carlo engine
  • Monte Carlo integration
  • Stochastic, Markov, Wiener, and processes
  • Option pricing
  • Markov chain Monte Carlo (MCMC) and Metropolis
  • Simulated annealing
  • Parallel Algorithms
  • Parallel architectures
  • Parallel metrics
  • Message passing
  • mpi4py
  • Master-Worker and Map-Reduce
  • pyOpenCL
  • Math Review and Notation
Pages : 388
Size : 4.6 MB
File type : PDF
Downloads: 143
Created: 2022-02-01
License: CC BY-NC-ND
Author(s): by Massimo Di Pierro
Annotated Algorithms in Python

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