String Algorithm

Table of Contents

1 Knuth–Morris–Pratt(KMP)

Match a pattern string P inside given long string T.

The idea is, when failure happens, we shift multiple position instead of just 1. We are able to do that because when the failure happens, we know what have been examined, so we have everything available to make the best choice. Specifically, we build a look-up table, for the pattern string. The table has an entry for each index of the string, describing the shift position. E.g., ABCABDA, the lookup table will be: 0000120. Actually the table refers to what's the substring matched the prefix of the pattern string.

The algorithm to build the table:

(defun build-table (pattern)
  (cl-loop with pos = 2
           with match = 0
           with size = (length pattern)
           with ret = (make-vector size 0)
           initially do
           ;; here I assume size is at least 2
           (assert (> size 1))
           (aset ret 0 -1)
           (aset ret 1 0)
           while (< pos size) do
           (if (equal (elt pattern (- pos 1)) (elt pattern match))
                 (aset ret pos (+ 1 match))
                 (incf match)
                 (incf pos))
             (if (> match 0)
                 (setq match (elt ret match))
               (aset ret pos 0)
               (incf pos)))
           finally return ret))

(ert-deftest build-table-test()
  (should (equal (build-table "AABAAAC") [-1 0 1 0 1 2 2]))
  (should (equal (build-table "ABCABCD") [-1 0 0 0 1 2 3])))

(ert-run-test (ert-get-test 'build-table-test))

1.1 214. Shortest Palindrome

Given a string S, you are allowed to convert it to a palindrome by adding characters in front of it. Find and return the shortest palindrome you can find by performing this transformation.

1.1.1 KMP

It is easy to convert the problem to find the longest Palindrome at the beginning of s. To apply KMP, we write the string as s + '#' + reverse(s). Then we build the KMP table for this string. What we need is to find the largest number inside KMP table.

1.1.2 brute force

I have a brute-force that "just" pass the tests.

class Solution {
  string shortestPalindrome(string s) {
    if (s.size() == 0) return s;
    if (s.size() == 1) return s;
    for (int i=(s.size()-1)/2;i>0;i--) {
      if (check(s, i, false)) {
        // std::cout << "success on " << i << " false"  << "\n";
        string sub = s.substr(i*2+2);
        std::reverse(sub.begin(), sub.end());
        return sub + s;
      } else if (check(s, i, true)) {
        // std::cout << "success on " << i << " true"  << "\n";
        // THREE
        // 1 2 3 4 5 6
        // - - -|- - -
        // 6/2=3
        // 1 2 3 4 5
        // - - | - -
        // i*2+1 - end
        string sub = s.substr(i*2+1);
        std::reverse(sub.begin(), sub.end());
        return sub + s;
    string sub;
    if (check(s, 0, false)) {
      sub = s.substr(2);
    } else {
      sub = s.substr(1);
    std::reverse(sub.begin(), sub.end());
    return sub + s;
   * on: pivot on idx or not
  bool check(string &s, int idx, bool on) {
    // std::cout << idx  << "\n";
    if (idx <0 || idx >= (int)s.size()) return false;
    int i=0,j=0;
    if (on) {
    } else {
      i = idx;
      j = idx+1;
    int size = s.size();
    while (i >= 0) {
      if (j >= size) return false;
      if (s[i] != s[j]) return false;
    return true;

2 Boyer Moore

It is a string match algorithm.

The rule lookup is in a hash table, which can be formed during proprocessing of pattern.

In the following examples, the lower case denote the matched or unmatched part for illustration purpose only. They are upper case when considering matching.

2.1 Bad Character Rule

Match from last. In the below example, the suffix MAN matches, but N does not match. Shift the pattern so that the first N (counted from last) go to the N place.

- - - - X - - K - - -
A N P A n M A N A M -
- N n A A M A N - - -
- - - N n A A M A N -

from right end to left. when a mismatch happens at `n`, find to left a `n`, then shift it to the position.

2.2 Good Suffix Rule

Similar to the bad rule, find the matched, in this case NAM. Then, if an failure happens, move the same part to the left of that match (in this case another NAM at the left) to that position.

- - - - X - - K - - - - -
M A N P A n a m A N A P -
A n a m P n a m - - - - -
- - - - A n a m P N A M -

when a mismatch happens, nam is the longest good suffix. Find nam to the left, and shift it to the position.

2.3 Galil Rule

As opposed to shifting, the Galil rule deals with speeding up the actual comparisons done at each alignment by skipping sections that are known to match. Suppose that at an alignment k1, P is compared with T down to character c of T. Then if P is shifted to k2 such that its left end is between c and k1, in the next comparison phase a prefix of P must match the substring T[(k2 - n)..k1]. Thus if the comparisons get down to position k1 of T, an occurrence of P can be recorded without explicitly comparing past k1. In addition to increasing the efficiency of Boyer-Moore, the Galil rule is required for proving linear-time execution in the worst case.

3 Rabin-Karp Algorithm

It is a string searching algorithm.

The Naive Solution for string search:

int func(char s[], int n, char pattern[], int m) {
  char *ps,*pp; //*
  for (i=0;i<n-m+1;) {
    if (*pp=='\0') return i; //*
    if (*ps == *pp) { //*
    } else {

The running time is \(O(mn)\).

The Rabin-Karp algorithm use hash for pattern match. First calculate hash(pattern). Then for every s[i,i+m-1], calculate the hash. Then compare them.

The key of the algorithm is the hash function. If the hash function need time m to compute, then it is still \(O(mn)\). If the collision happens often, then even if hash matches, we still need to verify.

Key point is to select a hast function, such that hash(i,i+m-1) can be computed by hash(i-1,i+m-2).

If add all characters' ASCII together, collision is often.

The used hash function is: select a large prime as base, 101 for example. Hash value is:

\begin{equation} hash("abc") = ASCII('a')*101^2 + ASCII('b')*101^1 + ASCII('c')*101^0 \end{equation}

Rabin-Karp is not so good for single string match because the worst case is \(O(mn)\), but it is the algorithm of choice for multiple pattern search.

K patterns, in a large string s, find any one of the K patterns.

3.1 Rolling Hash

3.1.1 Rabin-Karp rolling hash

3.1.2 Cyclic Polynomial (Buzhash)

s(a) means shift a left.

\begin{equation} H=s^{k-1}(h(c_1)) \oplus s^{k-2}(h(c_2)) \oplus \ldots \oplus s(h(c_{k-1})) \oplus h(c_k) \end{equation}

h is a tabulation hashing.

To remove \(c_1\) and add \(c_{k+1}\):

\begin{equation} H = s(H) \oplus s^k(h(c_1)) \oplus h(c_{k+1}) \end{equation}

3.2 Tabulation hashing

input key is p bits, output is q bits. choose a r less then p, and \(t=\lceil p/r \rceil\).

view a key as t r-bit numbers. Use a lookup table filled with random values to compute hash value for each of t numbers. Xor them together.

The choice of r should be made in such a way that this table is not too large, so that it fits into the computer's cache memory.

Author: Hebi Li

Created: 2017-03-27 Mon 14:36