# Dynamic Programming- Why Knuth's improvement to Optimal Binary Search Tree O(n^2)?

Posted on Oct 28, 2021

## Question

This is Exercise 15.5-4 of Introduction to Algorithms, 3rd edition, which is about Knuth’s improvement to the DP approach to Optimal Binary Search Tree.

The DP algorithm of Optimal Binary Search Tree is:

``````OPTIMAL_BST(p, q, n)
let e[1..n+1, 0..n], w[1..n+1, 0..n], and root[1..n, 1..n] be new tables
for i = 1 to n+1
e[i, i - 1] = q[i - 1];
w[i, i - 1] = q[i - 1];
for l = 1 to n
for i = 1 to n - l + 1
j = i + l - 1
e[i, j] = INFINITY
w[i, j] = w[i, j - 1] + p[j] + q[j]
for r = i to j
t = e[i, r - 1] + e[r + 1, j] + w[i, j]
if t < e[i, j]
e[i, j] = t
root[i, j] = r
return e and root
``````

The complexity is O(n3). Knuth had observed that `root[i, j - 1] <= root[i, j] <= root[i + 1, j]`, so Exercise 15.5-4 asks to implement an O(n2) algorithm by doing some modification to the original algorithm.

Well after some effort I have figured this out: in the innermost loop, replace the line

``````for r = i to j
``````

with

``````for r = r[i, j - 1] to r[i + 1, j]
``````

This has been proved by this link: Optimal binary search trees

However, I’m not sure this is really O(n2): since during each innermost loop, distance from r[i, j - 1] to r[i + 1, j] is not constant, I suspect it is still O(n3).

So my question is: can you please explain to me why the improvement to DP algorithm yields O(n2) complexity?

PS: Maybe I might have read Knuth’s paper first, but really I searched the web but found no free access to the paper.

You’re correct that the distance from `r[i, j - 1]` to `r[i + 1, j]` is not constant in the worst case, but it is constant on average, which suffices to imply a quadratic running time. The total number of iterations for `l` is