(i) Dynamic programming is an algorithmic design method that can be used when the solution to a problem can be viewed as the result of a sequence of decisions.
(ii) Dynamic programming approach is similar to divide and conquer. The given problem is divided into smaller and yet smaller possible sub-problems.
(iii) Dynamic programming is used whenever problems can be divided into similar sub-problems. So that their results can be re-used to complete the process.
(iv) Dynamic programming approaches are used to find the solution in optimized way. For every inner subproblem, dynamic algorithm will try to check the results of the previously solved sub-problems. The solutions of overlapped sub-problems are combined in order to get the better solution.

Asymptotic Notations are languages that uses meaningful statements about time and space complexity. The following three asymptotic notations are mostly used to represent time complexity of algorithms:
(i) Big O: Big O is often used to describe the worst -case of an algorithm.
(ii) Big \(\Omega \):Big Omega is the reverse Big O, if Big O is used to describe the upper bound (worst - case) of a asymptotic function, Big Omega is used to describe the lower bound (best -case).
(iii) Big \(\Theta \):When an algorithm has complexity with lower bound = upper bound, Say that an algorithm has a complexity O(n log n) and \(\Omega \) (n log n), it's actually has the complexity \(\Theta \) (n log n), which means the running time of that algorithm always falls in n log n in the best-case and worst-case.

(i) Time Factor -Time is measured by counting the number of key operations like comparisons in the sorting algorithm.
(ii) Space Factor -Space is measured by the maximum memory space required by the algorithm.

The complexity of an algorithm f (n) gives the running time and/or the storage space required by the algorithm in terms of n as the size of input data.
(i) Time Complexity: The Time complexity of an algorithm is given by the number of steps taken by the algorithm to complete the process.
(ii) Space Complexity: Space complexity of an algorithm is the amount of memory required to run to its completion.

(i) Input
(ii) Output
(iii) Finiteness
(iv) Definiteness
(v) Effectiveness
(vi) Correctness
(vii) Simplicity
(viii) Unambiguous
(ix) Feasibility
(x) Portable
(xi) Independent