Annotated Algorithms in Python

• 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