AOCP/Generating Permutations and Tuples
From charlesreid1
Algorithms for generating all permutations and tuples
Add-One Mixed Radix Generation Agorithm
Binary string permutations
Knuth starts simple - with binary strings.
Suppose we want to generate all 2^n binary strings of length n. How can we do that?
It is surprisingly easy - just start at 0, and keep adding 1 until you get to 11...11 (where there are n 1s). That's 2^n-1.
Decimal string permutations
If we have more than two objects, we can use the same approach as above. For example, suppose we have all ten decimal numbers, 0 through 9, and we want to generate all strings of length n that are permutations of these digits. There are 10^n such strings.
We can start at 0 base 10, and count up to 99...99 base 10 (where we start with n 0's and keep going until we have n 9's). That's 10^n-1.
Arbitrary string permutations
Suppose we want to run through all cases in which
where upper limits might be different for different components
This is a multiset problem, where we have the multiset
Then the task is essentially the same as repeatedly adding unity to the number in the mixed-radix number system
Algorithm M
Algorithm M = mixed radix generation algorithm
Algorithm for mixed-radix generation of permutations: this algorithm is a generalization of the "sequentially add one to the given number" approach
This algorithm visits all n-tuples by repeatedly adding 1 to the mixed-radix number until overflow occurs.
Note that the "visit" action is where we hand things off to the consumer (whoever is asking for permutations, to do whatever they are going to do).
Auxiliary variables a0 and m0 are introduced as well.
# Initialization set a_j <- 0 for 0 <= j <= n set m_0 <- 0 # Visit visit the n-tuple (a_1, ..., a_n) pass off control to the consumer # Prepare To Add One set j <- n # Carry If Necessary if a_j = m_j - 1, set a_j <- 0, j <- j-1 Repeat this step # Increase Unless Done if j=0: terminate else: a_j <- a_j + 1 return to Visit step
Note that if the number of slots (n) is small, we can write it out using nested for loops:
for a_1 in range 0 to (m_1 - 1): for a_2 in range 0 to (m_2 - 1): for a_3 in range 0 to (m_3 - 1): for a_4 in range 0 to (m_4 - 1): visit the n-tuple (a_1, a_2, a_3, a_4)
Gray Binary Code Generation Algorithm
Gray binary code provides an alternative way to generate permutations in non-lexicographic order. In particular, it produces permutations such that each permutation changes only one bit. For example, for n=4, we have 0000, 0001, 0011, 0010, 0111, 0101, 0100, etc.
These are important for converting between analog and digital.
Recurrence Relation
Let represent a gray binary sequence of n-bit strings. Then can be defined recursively by the rules:
where epsilon denotes the empty string, means the string prefixed with 0, and denotes teh sequence in reverse order with 1 prefixed to the string
The last string of equals the first string of so exactly one bit changes each step.
Alternative Formulation
Alternatively, we can define the sequence by giving an explicit formula to individual elements at various positions in the string.
For example:
This gives an explicit formula for individual elements g(k)
Then the infinite sequence
Is a permutation of all nonnegative integers. Thus, consists of the first 2^n elements of converted to n-bit strings by inserting 0s at left, if necessary.
Baudot Code
Baudot is a telegraph machine that uses 5 bits to represent each letter. The baudot telegraph machine uses gray code.
Chinese ring puzzle
Also known as tiring irons. The challenge is to remove rings from bar, but rings are interlocked in such a way that only two moves are possible:
- Rightmost ring can be removed or replaced
- Any other ring can be removed or replaced if the ring to its right is on the bar and all rings to the right of that one are off the bar.
The state of the puzzle can be represented with binary notation, 1 means ring is on the bar and 0 means ring is off the bar
Algorithm G
Algorithm G = gray binary generation algorithm
This algorithm visits all binary tuples
Start with and change one bit at a time
# Initialize set a_j <- 0 for 0 <= j < n set a_infty <- 0 # Visit visit the n-tuple (a_{n-1}, ..., a_1, a_0) # Change Parity set a_infty <- 1 - a_infty # Choose j if a_infty = 1: set j <- 0 else: let j >= 1 be minimum such that a_{j-1} = 1 after kth time performing this step, j = rho(k) (rho is the ruler function) # Complement Coordinate j if j = n: terminate else: a_j <- 1 - a_j return to Visit step
Flags
AOCP
The Art of Computer Programming notes from reading Donald Knuth's Art of Computer Programming
Part of the 2017 CS Study Plan.
Mathematical Foundations: AOCP/Infinite Series · AOCP/Binomial Coefficients · AOCP/Multinomial Coefficients AOCP/Harmonic Numbers · AOCP/Fibonacci Numbers Puzzles/Exercises:
Volume 2: Seminumerical Algorithms
Volume 3: Sorting and Searching AOCP/Combinatorics · AOCP/Multisets · Rubiks Cube/Permutations
AOCP/Combinatorial Algorithms · AOCP/Boolean Functions AOCP/Five Letter Words · Rubiks Cube/Tuples AOCP/Generating Permutations and Tuples
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Combinatorics
Combinatorics
Combinatorial Structures - Order Does Not Matter Ordinary generating functions
Labelled Structures - Order Matters Enumerating Permutations: String Permutations Generating Permutations: Cool · Algorithm M (add-one) · Algorithm G (Gray binary code)
Combinatorics Problems Longest Increasing Subsequence · Maximum Value Contiguous Subsequence · Racing Gems Cards (poker hands with a deck of 52 playing cards)
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Algorithms
Algorithms Part of Computer Science Notes
Series on Algorithms
Algorithms/Sort · Algorithmic Analysis of Sort Functions · Divide and Conquer · Divide and Conquer/Master Theorem Three solid O(n log n) search algorithms: Merge Sort · Heap Sort · Quick Sort Algorithm Analysis/Merge Sort · Algorithm Analysis/Randomized Quick Sort
Algorithms/Search · Binary Search · Binary Search Modifications
Algorithms/Combinatorics · Algorithms/Combinatorics and Heuristics · Algorithms/Optimization · Divide and Conquer
Algorithms/Strings · Algorithm Analysis/Substring Pattern Matching
Algorithm complexity · Theta vs Big O Amortization · Amortization/Aggregate Method · Amortization/Accounting Method Algorithm Analysis/Matrix Multiplication
Estimation Estimation · Estimation/BitsAndBytes
Algorithm Practice and Writeups Project Euler · Five Letter Words · Letter Coverage
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