A Novel Stack based Dynamic Programming for Reducing Memory Complexity Applied on DNA Sequences

programming partitions the problem into not completely independent sub problems and solves every sub problem just once and then saves its answer in a table in forward path. The required space for this table usually is proportional to the square of the input size that is contained a huge part of memory. In this paper we describe a new method for reducing the space complexity of dynamic programming. In this method, that information is saved in forward path, which they cannot reproduce at backward path. A stack is used for saving this data. By this way the path of constructing optimal solution can be reproduced by using saved information in stack. We can find some rules for selecting saved information. As an example we applied this method on Longest Common Subsequence (LCS) problem for global alignment of DNA sequences. As we examined in proposed algorithm, the size of stack in comparing to using space for LCS algorithm was reduced about 10 times and we could increase the input size in global alignment