The Art of Computer Programming by Knuth

1.2 Mathematical Preliminaries
"Mathematical notation is used for two main purposes in this book: to describe 
portions of an algorithm, and to analyze the performance characteristics of an 
algorithm."

1.2.1 Mathematical Induction

"
Suppose we want to prove that P(n) is true for all positive integers n.
 1 Give a proof that P(1) is true.
 2 Give a proof that "if all of P(1),P(2),...,P(n) are true, then P(n+1) is 
also true"; this proof should be valid for any positive integer n.
" Knuth's TAOCP V1E3 p.11

In N we have,
   |0
()
   |1
0
   |2
0 1
   |3
0 1 2
   |4
0 1 2 3
and so on (i.e. (|1+n)=(|n),n ) then P'1+|n or P(1+|n) or just P 1+|n gives 
(P 1),(P 2),(P 3),..,P n
So Knuth's description of "an important way to do this" where "this" is "to 
prove that P(n) is true for all positive integers n" can be rewritten 
(restated) as

1 Give a proof that P 1 is true.
2 Give a proof that "if all of P 1+|n are true then P 1+n is also true"; this 
proof should be valid for any positive integer n.

If we choose to begin with 0 instead of 1 then

1 Give a proof that P 0 is true.
2 Give a proof that "if all of P|n are true then P n is also true"; this proof 
should be valid for any positive integer n.

For people who don't like spaces between propositional functions and their 
arguments:

1 Give a proof that P.0 is true.
2 Give a proof that "if all P|n are true then P.n is also true"; this proof 
should be valid for any positive integer n.

For now, I will replace Knuth's notation with the appropriate N notation.

"
We can regard this method as an algorithmic proof procedure.
In fact, the following algorithm produces a proof of P.n for any positive 
integer n, assuming that steps 1 and 2 above have been worked out:

Algorithm I (Construct a proof).
Given a positive integer n, this algorithm will output a proof that P.n is true.
I1.[Prove P.0] Set k:0, and, according to 1, output a proof of P.0 .
I2.[k=n] If k=n, terminate the algorithm; the required proof has been output.
I3.[Prove P.k] According to 1, output a proof that if +/P|k then P.k . Also 
output "We have already proved P|k; hence P.k is true."
I4.[Increase k.] Increase k by 1 and go to step I2.
" Knuth's TAOCP V1E3 p.11-12

"
Algorithm E (Extended Euclid's Algorithm).
Given two positive integers m and n, we compute their greatest common divisor 
d, an we also compute two not-necessarily-positive integers a and b such that 
d=(a*m)+b*n.
E1.[Initialize.] Set ax:b:1, a:bx:0, c:m, d:n .
E2.[Divide.] q:c%d, r:c|d . (We have c=r+q*d and r ~ r > 0 and r ~ r < d.)
E3.[Remainder zero?] If 0=r, the algorithm terminates; we have in this case 
d=(a*m)+b*n as desired.
E4.[Recycle.] Set c:d, d:r, t:ax, ax:a, a:t-q*a, t:bx, bx:b, b:t-q*b, and go 
back to E2.
" Knuth's TAOCP V1E3 p.14


Contents

Volume 1 Fundamental Algorithms

Chapter 1 Basic Concepts

Algorithms

Mathematical Preliminaries
Mathematical Induction
Numbers, Powers, and Logarithms
Sums and Products
Integer Functions and Elementary Number Theory
Permutations and Factorials
Binomial Coefficients
Harmonic Numbers
Fibonacci Numbers
Generating Functions
Analysis of an Algorithm
Asymptotic Representations
The O-notation
Euler's summation formula
Some asymptotic calculations

MIX
Description of MIX
The MIX Assembly Language
Applications to Permutations

Some Fundamental Programming Techniques
Subroutines
Coroutines
Interpretive Routines
A MIX simulator
Trace Routines
Input and Output
History and Bibliography

Chapter 2 Information Structures

Introduction

Linear Lists
Stacks, Queues, and Deques
Sequential Allocation
Linked Allocation
Circular Lists
Doubly Linked Lists
Arrays and Orthogonal Lists

Trees
Traversing Binary Trees
Binary Tree Representation of Trees
Other Representations of Trees
Basic Mathematical Properties of Trees
Free Trees
Oriented Trees
The 'infinity lemma'
Enumeration of trees
Path Length
History and Bibliography

Multilink Structures
Dynamic Storage Allocation
History and Bibliography

Answers to Exercises


Volume 2 Seminumerical Algorithms

Chapter 3 Random Numbers

Introduction

Generating Uniform Random Numbers
The Linear Congruential Method
Choice of Modulus
Choice of multiplier
Potency
Other Methods

Statistical Tests
General Test Procedures for Studying Random Data
Empirical Tests
Theoretical Tests
The Spectral Test

Other Types of Random Quantities
Numerical Distributions
Random Sampling and Shuffling

What Is a Random Sequence?
Summary

Chapter 4 Arithmetic
Positional Number Systems

Floating Point Arithmetic
Single-Precision Calculations
Accuracy of Floating Point Arithmetic
Double Precision Calculations
Distribution of Floating Point Numbers

Multiple-Precision Arithmetic
The Classical Algorithms
Modular Arithmetic
How Fast Can We Multiply?

Radix Conversion

Rational Arithmetic
Fractions
The Greatest Common Divisor
Analysis of Euclid's Algorithm
Factoring into Primes

Polynomial Arithmetic
Division of Polynomials
Factorization of Polynomials
Evaluation of Powers
Evaluation of Polynomials

Manipulation of Power Series

Answer to Exercises


Volume 3 Sorting and Searching

Chapter 5 Sorting

Combinatorial Properties of Permutations
Inversions
Permutations of a Multiset
Runs
Tableaux and Involutions

Internal Sorting
Sorting by Insertion
Sorting by Exchanging
Sorting by Selection
Sorting by Merging
Sorting by Distribution

Optimum Sorting
Minimum-Comparison Sorting
Minimum-Comparison Merging
Minimum-Comparison Selection
Networks for Sorting

External Sorting
Multiway Merging and Replacement Selection
The Polyphase Merge
The Cascade Merge
Reading Tape Backwards
The Oscillating Sort
Practical Considerations for Tape Merging
External Radix Sorting
Two-Tape Sorting
Disks and Drums

Summary, History, and Bibliography

Chapter 6 Searching

Sequential Searching

Searching by Comparison of Keys
Searching an Ordered Table
Binary Tree Searching
Balanced Trees
Multiway Trees

Digital Searching
Hashing
Retrieval on Secondary Keys

Answer to Exercises


Volume 4 Combinatorial Algorithms

Chapter 7 Combinatorial Searching Part I

Zeros and Ones
Boolean Basics
Boolean Evaluation
Bitwise Tricks and Techniques
Binary Decision Diagrams

Generating All Possibilities
Generating Basic Combinatorial Patterns
Generating all n-tuples
Generating all permutations
Generating all combinations
Generating all partitions
Generating all set partitions
Generating all trees
History and further references

Answer to Exercises
Appendix D Index to Combinatorial Problems

Chapter 8 Recursion
Volume 5 Syntactical Algorithms
Chapter 9 Lexical Scanning
Chapter 10 Parsing
Volume 6 The Theory of Languages
Volume 7 Compilers

Sources
http://www-cs-faculty.stanford.edu/~uno/taocp.html