1. 0/1 Knapsack Making Change (any coin set)
Dynamic Programming
    0/1 Knapsack     Making Change (any coin set)

2. What is Dynamic Programming?
Dynamic programming is similar to divide-and-conquer in that the problem is broken down into smaller subproblems. In this approach we solve the small instances first, save the results, and look them up later when we needed, rather than recompute them.
Dynamic programming can sometimes provide an efficient solution to a problem for which divide-and-conquer produces an exponential run-time. Occasionally we find that we do not need to save all subproblem solutions. In these cases we can revise the algorithm greatly reducing the memory space requirements for the algorithm.
1. establish a recursive property that gives the solution to an instance of the problem
2. solve instances of the problem in a bottom-up fashion starting with the smaller instances first.

3. Binomial Theorem
The binomial theorem gives a closed-form expression for the coefficient of any term in the expansion of a binomial raised to the nth power.
(a+b)0 = 1
(a+b)1 = a+b
(a+b)2 = a2+2ab+b2
(a+b)3 = a3+3a2b+3ab2+b3
(a+b)4 = a4+4a3b+6a2b2+4ab3+b4

4. Counting Combinations & Pascal's Triangle
The binomial coefficient is also the number of combinations of n items taken k at a time, sometimes called n-choose-k. 1
1 1

5. Binomial Coefficient D&C Version
function bin(n,k : integer) return integer is
begin
if k=0 or k=n then
return 1;
else
return bin(n-1,k-1) + bin(n-1,k);
end if;
end bin;
This version of bin requires that the subproblems are recalculated many times for each recursive call.

6. Binomial Coefficient Dynamic Programming
function bin2(n,k:integer) return integer s
B : array(0..n,o..k) of integer;
begin
for i in 0..n loop
for j in 0..minimum(i,k) loop
if j=0 or j=i then
B(i,j)=1;
else
B(i,j):=B(i-1,j-1) + B(i-1,j);
end if;
end loop;
return B(n,k);
end bin2;
In bin2 the smallest instances of the problem are solved first and then used to compute values for the larger subproblems. Compare the computational complexities of bin and bin2.

7. Floyd's Algorithm for Shortest PathsV1 V2 V5 V4 V3 1 5 2 6 3
procedure floyd(W,D:matype) is
begin
D:=W;
for k in 1..n loop
for i in 1..n loop
for j in 1..n loop
D(i,j):=min(D(i,j),D(i,k)+D(k,j));
end loop;
end floyd;
Floyd's algorithm is very simple to implement. The fact that it works at all is not obvious. Be sure to work through the proof of algorithm correctness in the text.

8. Dynamic Programming - The Coin Changing Problem
Northeastern University - Javed A. Aslam - CSG713 Advanced Algorithms

9. Defining the Recurrence Relation
Northeastern University - Javed A. Aslam - CSG713 Advanced Algorithms

10. Implementing the Bottom-Up Algorithm
Northeastern University - Javed A. Aslam - CSG713 Advanced Algorithms

11. Generating an Optimal Solution
Northeastern University - Javed A. Aslam - CSG713 Advanced Algorithms

12. Algorithm/Program Analysis
Northeastern University - Javed A. Aslam - CSG713 Advanced Algorithms

13. Summary
Dynamic Programming solves problems that can be expressed as recurrence relations
Rather than recursive programming (e.g. D&C) DynPro solves these problem in a bottom-up fashion
It is good practice to compare complexity for D&C vs DynPro
DynPro works when sub-problems are solved by sub-solutions. That is, optimal partial solutions are parts of the optimal solution.