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Motivation: Given an array of N numbers, you need to support two operations. Operation 1: find-min(i, j) = return the minimum value in array[i ... j]. Operation 2: update(i, v) = update the value at array[i] to v. Solve the problem for N <= 10^6, number of operations <= 10^6.
To solve the above problem, both the operations need to run in O(log N) time, but using an naive array gives O(N) run-time for operation 1 (and O(1) run-time for operation 2). So how do you solve the problem? Read on. :)
Video tutorial: This is a superb tutorial, giving the motivation, walking through example, and going step-by-step through the pseudocode.
A sequence (array) is really just a function which associates integers (indices) with the corresponding values. However, there is no reason to restrict our usage of binary search to tangible sequences. In fact, we can use the same algorithm described above on any monotonic function f. ... The only difference is that we replace an array lookup with a function evaluation: we are now looking for some x such that f(x) is equal to the target value.
Since examples are the best way to go understand dynamic programming, here are three more classic dynamic programming problems. Make sure you either solve the each problem or try at least for a few hours before reading the solution.
Counting paths in a grid
You have a rectangular grid of points with n rows and n columns. You start at the bottom left corner. At each step, you can either go up to the next point in the same column or right to the next point in the same row. How many such paths are there from the bottom left corner to the top right corner?
What if some points are deleted (that is, no path ca...