Manacher's Algorithm - Finding all sub-palindromes in $O(N)$


Given string $s$ with length $n$. Find all the pairs $(i, j)$ such that substring $s[i\dots j]$ is a palindrome. String $t$ is a palindrome when $t = t_{rev}$ ($t_{rev}$ is a reversed string for $t$).

More precise statement

It's clear that in the worst case we can have $O(n^2)$ palindrome strings, and at the first glance it seems that there is no linear algorithm for this problem.

But the information about the palindromes can be kept in a more compact way: for each position $i = 0\dots n-1$ we'll find the values $d_1[i]$ and $d_2[i]$, denoting the number of palindromes accordingly with odd and even lengths with centers in the position $i$.

For instance, string $s = abababc$ has three palindromes with odd length with centers in the position $s[3] = b$, i. e. $d_1[3] = 3$:

$a\ \overbrace{b\ a\ \underbrace{b}_{s_3}\ a\ b}^{d_1[3]=3} c$

And string $s = cbaabd$ has two palindromes with even length with centers in the position $s[3] = a$, i. e. $d_2[3] = 2$:

$c\ \overbrace{b\ a\ \underbrace{a}_{s_3}\ b}^{d_2[3]=2} d$

So the idea is that if we have a sub-palindrome with length $l$ with center in some position $i$, we also have sub-palindromes with lengths $l-2$, $l-4$ etc. with centers in $i$. So these two arrays $d_1[i]$ and $d_2[i]$ are enough to keep the information about all the sub-palindromes in the string.

It's a surprising fact that there is an algorithm, which is simple enough, that calculates these "palindromity arrays" $d_1[]$ and $d_2[]$ in linear time. The algorithm is described in this article.


In general, this problem has many solutions: with String Hashing it can be solved in $O(n\cdot \log n)$, and with Suffix Trees and fast LCA this problem can be solved in $O(n)$.

But the method described here is sufficiently simpler and has less hidden constant in time and memory complexity. This algorithm was discovered by Glenn K. Manacher in 1975.

Trivial algorithm

To avoid ambiguities in the further description we denote what "trivial algorithm" is.

It's the algorithm that does the following. For each center position $i$ it tries to increase the answer by one until it's possible, comparing a pair of corresponding characters each time.

Such an algorithm is slow, it can calculate the answer only in $O(n^2)$.

The implementation of the trivial algorithm is:

vector<int> d1(n),  d2(n);
for (int i = 0; i < n; i++) {
    d1[i] = 1;
    while (0 <= i - d1[i] && i + d1[i] < n && s[i - d1[i]] == s[i + d1[i]]) {

    d2[i] = 0;
    while (0 <= i - d2[i] - 1 && i + d2[i] < n && s[i - d2[i] - 1] == s[i + d2[i]]) {

Manacher's algorithm

We describe the algorithm to find all the sub-palindromes with odd length, i. e. to calculate $d_1[]$. The solution for all the sub-palindromes with even length (i.e. calculating the array $d_2[]$) will be a minor modification for this one.

For fast calculation we'll maintain the borders $(l, r)$ of the rightmost found sub-palindrome (i. e. the palindrome with maximal $r$). Initially we set $l = 0, r = -1$.

So, we want to calculate $d_1[i]$ for the next $i$, and all the previous values in $d_1[]$ have been already calculated. We do the following:

Again, we should not forget to update the values $(l, r)$ after calculating each $d_1[i]$.

Also we'll repeat that the algorithm was described to calculate the array for odd palindromes $d_1[]$, the algorithm is similar for the array of even palindromes $d_2[]$. The required modifications can be seen in the code below.

Complexity of Manacher's algorithm

At the first glance it's not obvious that this algorithm has linear time complexity, because we often run the naive algorithm while searching the answer for a particular position.

However, a more careful analysis shows that the algorithm is linear. In fact, Z-function building algorithm, which looks similar to this algorithm, also works in linear time.

We can notice that every iteration of trivial algorithm increases $r$ by one. Also $r$ cannot be decreased during the algorithm. So, trivial algorithm will make $O(n)$ iterations in total.

Also, other parts of Manacher's algorithm work obviously in linear time. Thus, we get $O(n)$ time complexity.

Implementation of Manacher's algorithm

For calculating $d_1[]$, we get the following code. Things to note:

vector<int> d1(n);
for (int i = 0, l = 0, r = -1; i < n; i++) {
    int k = (i > r) ? 1 : min(d1[l + r - i], r - i + 1);
    while (0 <= i - k && i + k < n && s[i - k] == s[i + k]) {
    d1[i] = k--;
    if (i + k > r) {
        l = i - k;
        r = i + k;

For calculating $d_2[]$, the code looks similar, but with minor changes in arithmetical expressions. Things to note:

vector<int> d2(n);
for (int i = 0, l = 0, r = -1; i < n; i++) {
    int k = (i > r) ? 0 : min(d2[l + r - i + 1], r - i + 1);
    while (0 <= i - k - 1 && i + k < n && s[i - k - 1] == s[i + k]) {
    d2[i] = k--;
    if (i + k > r) {
        l = i - k - 1;
        r = i + k ;