# Week 4-5: Generating Permutations and Combinations

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1 Week 4-5: Generating Permutations and Combinations March 7, Generating Permutations We have learned that there are n! permutations of {1, 2,..., n}. It is important in many instances to generate a list of such permutations. For example, for the permutation 3142 of {1, 2, 3, 4}, we may insert 5 in 3142 to generate five permutations of {1, 2, 3, 4, 5} as follows: 53142, 35142, 31542, 31452, If we have a complete list of permutations for {1, 2,..., n 1}, then we can obtain a complete list of permutations for {1, 2,..., n} by inserting the number n in n ways to each permutation of the list for {1, 2,..., n 1}. For n = 1, the list is just 1 For n = 2, the list is For n = 3, the list is

2 To generate a complete list of permutations for the set {1, 2,..., n}, we assign a direction to each integer k {1, 2,..., n} by writing an arrow above it pointing to the left or to the right: k or k. We consider permutations of {1, 2,..., n} in which each integer is given a direction; such permutations are called directed permutations. An integer k in a directed permutation is called mobile if its arrow points to a smaller integer adjacent to it. For example, for , the integers 3, 5, and 6 are mobile. It follows that 1 can never be mobile since there is no integer in {1, 2,..., n} smaller than 1. The integer n is mobile, except two cases: (i) n is the first integer and its arrow points to the left, i.e., n ; (ii) n is the last integer and its arrow points to the right, i.e., n. 2

3 For n = 4, we have the list Algorithm 1.1. Algorithm for Generating Permutations of {1, 2,..., n}: Step 0. Begin with 1 2 n. Step 1. Find the largest mobile integer m. Step 2. Switch m and the adjacent integer its arrow points to. Step 3. Switch the directions for all integers p > m. Step 4. Write down the resulting permutation with directions and return to Step 1. Step 5. Stop if there is no mobile integer. 3

4 For example, for n = 2, we have 1 2 and 2 1. For n = 3, we have 1 2 3, 1 3 2, 3 1 2, 3 2 1, 2 3 1, For n = 4, the algorithm produces the list Proof. Observe that when n is not the largest mobile the direction of n must be either like n or n in the permutation. When the largest mobile m (with m < n) is switched with its target integer to produce a new permutation, the direction of n will be changed simultaneously, and the permutation with direction becomes n or n. Now n is the largest mobile. Switching n with its target integer for n 1 times to produce n 1 more permutations, we obtain exactly n new permutations (including the one before switching n). Each member k (2 k n) moves k 1 times from right to left, then k 1 times from left to right, and goes in this way from one side to the other for (k 1)! times (which is an even number when k 3); the total number of moves of k is (k 1)!(k 1). Thus the total number of moves is n (k 1)!(k 1), k=1 which is n! 1 by induction on n. The algorithm stops at the permutation n. 4

5 2 Inversions of Permutations Let u 1 u 2... u n be a permutation of S = {1, 2,..., n}. We can view u 1 u 2... u n as a bijection π : S S defined by π(1) = u 1, π(2) = u 2,..., π(n) = u n. If u i > u j for some i < j, the ordered pair (u i, u j ) is called an inversion of π. The number of inversions of π is denoted by inv(π). For example, the permutation of {1, 2,..., 7} has the inversions: (2, 1), (3, 1), (4, 1), (3, 2), (6, 5), (7, 5), (7, 6). For k {1, 2,..., n} and u j = k, let a k be the number of integers that precede k in the permutation u 1 u 2... u n but greater than k, i.e., a k = #{u i : i < j, u i > u j = k} = #{π(i) : i < j, π(i) > π(j) = k}. It measures how much k is out of order by counting the number of integers larger than k but located before k. The tuple (a 1, a 2,..., a n ) is called the inversion sequence (or inversion table) of the permutation π = u 1 u 2... u n. The sum inv(π) := a 1 + a a n measures the total disorder of a permutation. Example 2.1. The inversion sequence of the permutation of {1, 2,..., 7} is (3, 1, 0, 0, 2, 1, 0). It is clear that for any permutation π of {1, 2,..., n}, the inversion sequence (a 1, a 2,..., a n ) of π satisfies 0 a 1 n 1, 0 a 2 n 2,..., 0 a n 1 1, a n = 0. (1) It is easy to see that the number of sequences (a 1, a 2,..., a n ) satisfying (1) equals n (n 1) 2 1 = n!. This suggests that the inversion sequences may be characterized by (1). 5

6 Theorem 2.1. Let (a 1, a 2,..., a n ) be an integer sequence satisfying 0 a 1 n 1, 0 a 2 n 2,..., 0 a n 1 1, a n = 0. Then there is a unique permutation π of {1, 2,..., n} whose inversion sequence is (a 1, a 2,..., a n ). Proof. We give two algorithms to uniquely construct the permutation whose inversion sequence is (a 1, a 2,..., a n ). Algorithm I. Construction of a Permutation from Its Inversion Sequence: Step 0. Write down n. Step 1. If a n 1 = 0, place n 1 before n; if a n 1 = 1, place n 1 after Step 2. n. If a n 2 = 0, place n 2 before the two members n and n 1; if a n 2 = 1, place n 2 between n and n 1; if a n 2 = 2, place. n 2 after both n and n 1. Step k. If a n k = 0, place n k to the left of the first position; if a n k = 1, place n k to the right of the 1st existing number; if a n k = 2, place n k to the right of the 2nd existing number;...; if a n k = k, place n k to the right of the last existing number. In general, insert n k to the right of the a n k -th. existing number. Step n 1. If a 1 = 0, place 1 before all existing numbers; otherwise, place 1 to the right of the a 1 th existing number. For example, for the inversion sequence (a 1, a 2,..., a 8 ) = (4, 6, 1, 0, 3, 1, 1, 0), 6

7 its permutation can be constructed by Algorithm I as follows: 8 Write down Since a 7 = 1, insert 7 to the right of the first number Since a 6 = 1, insert 6 to the right of the first number Since a 5 = 3, insert 5 to the right of the third number Since a 4 = 0, insert 4 to the left of the first number Since a 3 = 1, insert 3 to the right of the first number Since a 2 = 6, insert 2 to the right of the sixth number Since a 1 = 4, insert 1 to the right of the fifth number 6. Algorithm II. Construction of a Permutation from Its Inversion Sequence: Step 0. Mark down n empty spaces. Step 1. Put 1 into the (a 1 + 1)-th empty space from left. Step 2. Put 2 into the (a 2 + 1)-th empty space from left.. Step k. Put k into the (a k + 1)-th empty space from left.. Step n. Put n into the (a n + 1)-th empty space (the last empty box) from left. For example, the permutation for the inversion sequence (a 1, a 2,..., a 8 ) = (4, 6, 1, 0, 3, 1, 1, 0) can be constructed by Algorithm II as follows: Mark down 8 empty spaces. 1 Since a 1 = 4, put 1 into the 5th empty space. 1 2 Since a 2 = 6, put 2 into the 7th empty space Since a 3 = 1, put 3 into the 2nd empty space Since a 4 = 0, put 4 into the 1st empty space Since a 5 = 3, put 5 into the 4th empty space Since a 6 = 1, put 6 into the 2nd empty space Since a 7 = 1, put 7 into the 2nd empty space Since a 8 = 0, put 8 into the 1st empty space. 7

8 3 Generating Combinations Let S be an n-set. For convenience of generating combinations of S, we take S to be the set S = {x n 1, x n 2,..., x 2, x 1, x 0 }. Each subset A of S can be identified as a function χ A : S {0, 1}, called the characteristic function of A, defined by { 1 if x A χ A (x) = 0 if x A. In practice, χ A is represented by a 0-1 sequence or a base 2 numeral. example, for S = {x 7, x 6, x 5, x 4, x 3, x 2, x 1, x 0 }, {x 7, x 5, x 2, x 1 } {x 6, x 5, x 3, x 1, x 0 } {x 7, x 6, x 5, x 4, x 3, x 2, x 1, x 0 } Algorithm 3.1. The algorithm for Generating Combinations of {x n 1,..., x 1, x 0 }: Step 0. Begin with a n 1 a 1 a 0 = Step 1. If a n 1 a 1 a 0 = 1 11, stop. Step 2. If a n 1 a 1 a , find the smallest integer j such that a j = 0. Step 3. Change a j, a j 1,..., a 0 (either from 0 to 1 or from 1 to 0), write down a n 1 a 1 a 0, and return to Sept 1. For n = 4, the algorithm produces the list The unit n-cube Q n is a graph whose vertex set is the set of all 0-1 sequences of length n, and two sequences are adjacent if they differ in only one place. A Gray code of order n is a path of Q n that visits every vertex of 8 For

9 Figure 1: 4-dimensional cube. Q n exactly once, i.e., a Hamilton path of Q n. For example, is a Gray code of order 3. It is obvious that this Gray code can not be a part of any Hamilton cycle since 000 and 111 are not adjacent. A cyclic Gray code of order n is a Hamilton cycle of Q n. For example, the closed path is a cyclic Gray code of order 3. For n = 1, we have the Gray code 0 1. For n = 2, we use 0 1 to produce the path by adding a 0 in the front, and use 1 0 to produce by adding a 1 in the front, then combine the two paths to produce the Gray code For n = 3, we use the Gray code (of order 2) to produce the path by adding 0 in the front, and use the Gray code (the reverse of ) to produce the path by adding 1 in the front. Combine 9

10 the two paths to produce the Gray code of order The Gray codes obtained in this way are called reflected Gray codes. Algorithm 3.2. Algorithm of generating a Reflected Gray Code of order n: Step 0. Begin with a n a n 1 a 2 a 1 = Step 1. If a n a n 1 a 2 a 1 = 10 00, stop. Step 2. If a n +a n 1 + +a 2 +a 1 = even, then change a 1 (either from 0 to 1 or from 1 to 0), i.e., set a 1 := 1 a 1. Step 3. If a n + a n 1 + a 2 + a 1 = odd, find the smallest index j such that a j = 1 and change a j+1 (either from 0 to 1 or from 1 to 0), i.e., set a j+1 := 1 a j+1. Step 4. Write down a n a n 1 a 2 a 1 and return to Step 1. We note that if a n a n 1 a and a n + a n a 1 = odd, then j n 1 so that j + 1 n and a j+1 is defined. We also note that the smallest number j in Step 3 may be 1, i.e., a 1 = 1; if so there is no i < j such that a i = 0 and we change a j+1 = a 2 as instructed in Step 3. Proof. We proceed by induction on n. For n = 1, it is obviously true. For n = 2, we have Let n 3 and assume that it is true for 1, 2,..., n 1. (1) When the algorithm is applied, by the induction hypothesis the first resulted 2 n 1 words form the reflected Gray code of order n 1 with a 0 attached to the head of each word; the 2 n 1 -th word is (2) Continuing the algorithm, we have Now for each word of the form 11b n 2 b 1, the parity of 11b n 2 b 1 is the same as the parity of b n 2 b 1. Continuing the algorithm, the next 2 n 2 words (including 110 0) form a reflected Gray code (of order n 2) with a 11 attached at the beginning; the last word is (3) Continuing the algorithm, we have

11 The next 2 n 3 words (including ) form a reflected Gray code (of order n 3) with a 101 attached at the beginning; the last word is (4) ; there are 2 n 4 words with 1001 attached at the beginning.. (n 2) Continuing the algorithm, we have The next 2 2 words (including ) form a reflected Gray code (of order 2) with attached at the beginning; the last word is (n 1) ; there are 2 1 words (of order 1), , with attached at the beginning. (n) The algorithm produces 1 word (n + 1) Finally, the algorithm ends at Notice that all words produced from Step (k) to Step (n + 1) inclusive are distinct from the words produced in Step (k 1), where 2 k n + 1. Thus the words produced by the algorithm are distinct and the total number of words is 2 n n = 2 n. This implies that the sequence of the words produced forms a Gray code of order n. Next we show that the words resulted in the second half of the algorithm are the words obtained from the reversing of the Gray codes of order n 1 with 1 attached in the front. Consider two consecutive words of length n in the second half of the algorithm, say, We need to show that 1 a n 1 a n 2 a 2 a 1 1 b n 1 b n 2 b 2 b 1. b n 1 b n 2 b 2 b 1 a n 1 a n 2 a 2 a 1 by the same algorithm for n 1. Note that 1 a n 1 a n 2 a 2 a 1 and 1 b n 1 b n 2 b 2 b 1 have opposite parity. It follows that a n 1 a n 2 a 2 a 1 and b n 1 b n 2 b 2 b 1 have opposite parity. We have two cases. 11

12 (1) b n 1 b n 2 b 2 b 1 is even. Then a n 1 a n 2 a 2 a 1 is odd. It follows that 1 a n 1 a n 2 a 2 a 1 is even. By definition of the algorithm, we have b n 1 = a n 1, b n 2 = a n 2,..., b 2 = a 2, and b 1 = 1 a 1 ; i.e., a n 1 = b n 1,..., a 2 = b 2, and a 1 = 1 b 1. This means that b n 1 b n 2 b 2 b 1 a n 1 a n 2 a 2 a 1. (2) b n 1 b n 2 b 2 b 1 is odd. Then a n 1 a n 2 a 2 a 1 is even. It follows that 1 a n 1 a n 2 a 2 a 1 is odd. Note that all of a 1,..., a n 1 are nonzero, since it is not stopped yet. Since 1 a n 1 a n 2 a 2 a 1 1 b n 1 b n 2 b 2 b 1, then by definition of the algorithm, there exists an index j (1 j n 2) such that a 1 = = a j 1 = b 1 = = b j 1 = 0, a j = b j = 1, and b j+1 = 1 a j+1, b j+2 = a j+2,..., b n 1 = a n 1. Note that a j+1 = 1 b j+1. We see that b n 1 b n 2 b 2 b 1 a n 1 a n 2 a 2 a 1 by definition of the algorithm. 4 Generating r-combinations Let S = {1, 2,..., n}. When an r-combination or an r-subset A = {a 1, a 2,..., a r } of S is given, we always assume that a 1 < a 2 < < a r. For two r- combinations A = {a 1, a 2,..., a r } and B = {b 1, b 2,..., b r } of S, if there is an integer k (1 k r) such that a 1 = b 1, a 2 = b 2,..., a k 1 = b k 1, a k < b k, we say that A precedes B in the lexicographic order, written A < B. Then the set P r (S) of all r-subsets of S is linearly ordered by the lexicographic order. For simplicity, we write an r-combination {a 1, a 2,..., a r } as an r-permutation a 1 a 2 a r with a 1 < a 2 < < a r. Theorem 4.1. Let a 1 a 2 a r be an r-combination of {1, 2,..., n}. The first r-combination in lexicographic order is 12 r, and the last r-combination in lexicographic order is (n r + 1) (n 1)n. If a 1 a 2 a k a r (n r + 1) (n 1)n and k is the largest index such that a k n r + k, then the successor of a 1 a 2 a r is a 1 a 2 a k 1 (a k + 1)(a k + 2) (a k + r k + 1). 12

13 Proof. Since a i (n r + i) for all 1 i r, then a k n r + k implies a k < n r + k. Hence a k + r k + 1 < n + 1. Algorithm 4.2. Algorithm for Generating r-combinations of {1, 2,..., n} in Lexicographic Order: Step 0. Begin with the r-combination a 1 a 2 a r = 12 r. Step 1. If a 1 a 2 a r = (n r + 1) (n 1)n, stop. Step 2. If a 1 a 2 a r (n r + 1) (n 1)n, find the largest k such that a k < n r + k. Step 3. Change a 1 a 2 a r to a 1 a k 1 (a k + 1)(a k + 2) (a k + r k + 1), write down a 1 a 2 a r, and return back to Step 1. Example 4.1. The collection of all 4-combinations of {1, 2, 3, 4, 5, 6} are listed by the algorithm: Theorem 4.3. Let a 1 a 2 a r be an r-combination of {1, 2,..., n}. Then the number of r-combinations up to the place a 1 a 2 a r in lexicographic order equals ( ) ( ) ( ) ( ) ( ) n n a1 n a2 n ar 1 n ar. r r r Proof. The r-combinations b 1 b 2 b r after a 1 a 2 a r can be classified into r kinds: (1) b 1 > a 1 ; there are ( n a 1 r (2) b 1 = a 1, b 2 > a 2 ; there are ( n a 2 r 1 (3) b 1 = a 1, b 2 = a 2, b 3 > a 3 ; there are ( n a 3 r 2. ) such r-combinations. ) such r-combinations. ) such r-combinations. (r 1) b 1 = a 1,..., b r 2 = a r 2, b r 1 > a r 1 ; there are ( n a r 1 2 r-combinations. ) such (r) b 1 = a 1,..., b r 1 = a r 1, b r > a r ; there are ( n a r ) 1 such r-combinations. Since the number of r-combinations of {1, 2,..., n} is ( ) n r, the conclusion follows immediately. 13

14 Example 4.2. The 3-combinations of {1, 2, 3, 4, 5} are as follows: 123, 124, 125, 134, 135, 145, 234, 235, 245, 345 The 3-permutations of {1, 2, 3, 4, 5} can be obtained by making 3! permutations for each 3-combination: Partially Ordered Sets Definition 5.1. A (binary) relation on a set X is a subset R X X. Two members x and y of X are said to satisfy the relation R, written xry, if (x, y) R. A relation R on X is said to be (1) reflexive if xrx for all x X; (2) irreflexive if x Rx (also write x \Rx) for all x X, where R = (X X) R. (3) symmetric provided that if xry then yrx; (4) antisymmetric provided that if x y and xry then y Rx (equivalently, if xry and yrx then x = y); (5) transitive provided that if xry and yrz then xrz; (6) complete provided that if x, y X and x y then either xry or yrx. Example 5.1. (1) The relation of set containment,, is a reflexive and transitive relation on the power set P (X) of all subsets of X. (2) The relation of divisibility, denoted, is a reflexive and transitive relation on the set Z of integers. 14

15 A partial order on a set X is a reflexive, antisymmetric, and transitive relation, that is, (P1) x x for all x, (P2) x y and y x then x = y, (P3) if x y and y z then x z. A strict partial order on a set X is an irreflexive and transitive relation, that is, (SP1) x x for all x, and (SP2) if x < y and y < z then x < z. If a relation R is a partial order, which is usually denoted by, then the relation <, defined by a < b iff a b but a b, is a strict partial order. Conversely, for a strict partial order < on a set X, the relation defined by a b iff a < b or a = b is a partial order. A set X with a partial order is called a partially ordered set (or poset for short), denoted (X, ). A linear order on a set X is a partial order such that either a b or b a for any two members a and b of X, i.e., a complete partial order. A strict linear order is an irreflexive, transitive, and complete relation. A preference relation is a relation which is reflexive and transitive. An equivalence relation is a reflexive, symmetric, and transitive relation. Example 5.2. Let S = {1, 2, 3, 4}. Then relation larger than in its ordinary meaning is the relation > = { (2, 1), (3, 1), (4, 1), (3, 2), (4, 2), (4, 3) }, and it is a strict linear order relation. The relation less than or equal to in its ordinary meaning is the relation = { (1, 1), (1, 2), (1, 3), (1, 4), (2, 2), (2, 3), (2, 4), (3, 3), (3, 4), (4, 4) }, and is a linear order relation. 15

16 Example 5.3. Let C denote the set of complex numbers. Consider the relation R on C given by zrw if z = w, where z = a + ib and z = a 2 + b 2. Then R is an equivalence relation on C. The relation L on C, defined by zlw (where z = a + ib and w = c + id) if a < c or a = c but b d, is a linear order on C. Example 5.4. Let V be a vector space over R. A partial order on V is said to be compatible with the addition and scalar multiplication provided that (i) if u v then cu cv for all c 0; (ii) if u 1 v 1 and u 2 v 2 then u 1 + u 2 v 1 + v 2. The property (ii) implies the following translation preserving property (iii) if u v then u + w v + w for all w V. A strongly convex cone of V is a nonempty subset C V satisfying (a) if u C and c 0 then cu C; (b) if u, v C then u + v C; (c) if u C and is nonzero, then u C, i.e., if u, u C then u = 0. Given a strongly convex cone C of V. The relation on V, defined by u v iff v u C, is a partial order compatible with the addition and scalar multiplication. Clearly, (i) u u for all u V ; (ii) if u v and v u then u = v; (iii) u v and v w, then u w; (iv) if u v then u + w v + w for all w V. Conversely, given a partial order on V compatible with the addition and scalar multiplication. The subset C := {v V v 0} is a strongly convex cone of V. Let H be a subspace of V. Then the relation on V, defined by u v if v u H, is an equivalence relation on V. 16

17 Let 1 and 2 be two partial orders on a set X. The poset (X, 2 ) is called an extension of the poset (X, 1 ) if, whenever a 1 b, then a 2 b. In particular, an extension of a partial order has more compatible pairs. We show that every finite poset has a linear extension, that is, an extension which is a linearly ordered set. Theorem 5.2. Let (X, ) be a finite partially ordered set. Then there is a linear order such that (X, ) is an extension of (X, ). Proof. We need to show that the elements of X can be listed in some order x 1, x 2,..., x n in such a way that if x i x j then x i comes before x j in this list, i.e., i j. The following algorithm does the job. Algorithm 5.3. Algorithm for a Linear Extension of an n-poset: Step 1. Choose a minimal element x 1 from X (with respect to the ordering ; if such elements are not unique, choose any one). Step 2. Delete x 1 from X; choose a minimal element x 2 from X {x 1 }. Step 3. Delete x 2 from X {x 1 }; choose a minimal element x 3 from X {x 1, x 2 }.. Step n. Delete x n 1 from X {x 1,..., x n 2 } and choose the only element x n in X {x 1,..., x n 1 }. Let R be an equivalence relation on a set X. For each element x X, we call the set [x] = {y X : xry} an equivalence class of R and x a representative of the class [x]. Theorem 5.4. Let R be an equivalence relation on a set X. Then for any x, y X, the following statements are logically equivalent: 1) [x] [y] ; 2) [x] = [y]; 3) xry. 17

18 A collection P = {A 1, A 2,..., A k } of nonempty subsets of a set X is called a partition of X if A i A j = for i j and X = k i=1 A i. We will show below that if R is an equivalence relation on a set X, then the collection P R = { [x] : x X } is a partition of X. Conversely, if P = {A 1, A 2,..., A k } is a partition of X, then the relation k R P = A i A i is an equivalence relation on X. i=1 Theorem 5.5. Let R be an equivalence relation on a set X, and let P = {A 1, A 2,..., A k } be a partition of X. Then (a) P R is a partition of X. (b) R P is an equivalence relation on X. (c) R PR = R, P RP = P. Proof. Parts (a) and (b) are trivial. We only prove Part (c). Let (x, y) R PR. Then, by definition of equivalence relation induced from the partition P R, there exists a part A P R such that x, y A. Note that the parts in P R are the equivalence classes [x] of the equivalence relation R; in particular A is an equivalence class of R. Since A contains both x and y, it follows that A = [x] = [y]. So (x, y) R. Hence R PR R. Let (x, y) R. Then [x] = [y] is a part of P R. Thus [x] [y] R PR. Since (x, y) [x] [y], we see that (x, y) R PR. Therefore R R PR. A P RP A is an equivalence class of the relation R P A is a part of the partition P. Example 5.5. Let V be a vector space over R. An equivalence relation on V is said to be translation preserving if u v implies u + w v + w for all w V. A subspace of V is a nonempty subset H V such that if u, v H then au + bv H for all a, b R. 18

19 Given a subspace H V. The relation on V, defined by u v if v u H, is an order preserving equivalence relation on V. 19

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