Algorithms

# Types of problems that can be solved using Dynamic Programming 0
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These are the 10 types of problems that can be solved using Dynamic Programming.

0-1 Kanpsack

Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. In other words, given two integer arrays val[0..n-1] and wt[0..n-1] which represent values and weights associated with n items respectively. Also given an integer W which represents knapsack capacity, find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to W. You cannot break an item, either pick the complete item or don’t pick it (0-1 property).

Unbounded Knapsack

Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. In other words, given two integer arrays val[0..n-1] and wt[0..n-1] which represent values and weights associated with n items respectively. Also given an integer W which represents knapsack capacity, find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to W. You cannot break an item, either pick the complete item or don’t pick it (0-1 property).

Fibonacci

The Fibonacci Sequence is the series of numbers:

0, 1, 1, 2, 3, 5, 8, 13, 21, 34, …

The next number is found by adding up the two numbers before it:

• the 2 is found by adding the two numbers before it (1+1),
• the 3 is found by adding the two numbers before it (1+2),
• the 5 is (2+3),
• and so on!

Example: the next number in the sequence above is 21+34 = 55

LCS

Given two sequences, find the length of longest subsequence present in both of them. A subsequence is a sequence that appears in the same relative order, but not necessarily contiguous. For example, “abc”, “abg”, “bdf”, “aeg”, ‘”acefg”, .. etc are subsequences of “abcdefg”.

LIS

The Longest Increasing Subsequence (LIS) problem is to find the length of the longest subsequence of a given sequence such that all elements of the subsequence are sorted in increasing order. For example, the length of LIS for {10, 22, 9, 33, 21, 50, 41, 60, 80} is 6 and LIS is {10, 22, 33, 50, 60, 80}.

Largest Sum Contiguous Subarray – Write an efficient program to find the sum of contiguous subarray within a one-dimensional array of numbers that has the largest sum.

Matrix Chain Multiplication

Given a sequence of matrices, find the most efficient way to multiply these matrices together. The problem is not actually to perform the multiplications, but merely to decide in which order to perform the multiplications.
We have many options to multiply a chain of matrices because matrix multiplication is associative. In other words, no matter how we parenthesize the product, the result will be the same. For example, if we had four matrices A, B, C, and D, we would have:

`(ABC)D = (AB)(CD) = A(BCD) = ....`

DP on Trees

Given a tree with N nodes and N-1 edges, calculate the maximum sum of the node values from root to any of the leaves without re-visiting any node.

DP on Grid

Finding the Minimum Cost Path in a Grid when a Cost Matrix is given.
Finding the number of ways to reach from a starting position to an ending position travelling in specified directions only.
Finding the number of ways to reach a particular position in a grid from a starting position (given some cells which are blocked)

Others

Any other category of problems you can suggest.

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