Question

I’m re-reading Skiena’s Algorithm Design Manual to catch up on some stuff I’ve forgotten since school, and I’m a little baffled by his descriptions of Dynamic Programming. I’ve looked it up on Wikipedia and various other sites, and while the descriptions all make sense, I’m having trouble figuring out specific problems myself. Currently, I’m working on problem 3-5 from the Skiena book. (Given an array of n real numbers, find the maximum sum in any contiguous subvector of the input.) I have an O(n^2) solution, such as described in this answer. But I’m stuck on the O(N) solution using dynamic programming. It’s not clear to me what the recurrence relation should be.

I see that the subsequences form a set of sums, like so:

S = {a,b,c,d}

a    a+b    a+b+c    a+b+c+d
     b      b+c      b+c+d
            c        c+d
                     d

What I don’t get is how to pick which one is the greatest in linear time. I’ve tried doing things like keeping track of the greatest sum so far, and if the current value is positive, add it to the sum. But when you have larger sequences, this becomes problematic because there may be stretches of negative numbers that would decrease the sum, but a later large positive number may bring it back to being the maximum.

I’m also reminded of summed area tables. You can calculate all the sums using only the cumulative sums: a, a+b, a+b+c, a+b+c+d, etc. (For example, if you need b+c, it’s just (a+b+c) – (a).) But don’t see an O(N) way to get it.

Can anyone explain to me what the O(N) dynamic programming solution is for this particular problem? I feel like I almost get it, but that I’m missing something.

Answer 1

You should take a look to this pdf back in the school in http://castle.eiu.edu here it is:

The explanation of the following pseudocode is also int the pdf.