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# UNDERSTANDING AND OVERCOMING INTRACTABILITY IN ALGORITHMS DESIGN

UNDERSTANDING AND OVERCOMING INTRACTABILITY IN ALGORITHMS DESIGN.

380CT: THEORETICAL ASPECTS OF COMPUTER SCIENCE- COURSEWORK 2
UNDERSTANDING AND OVERCOMING INTRACTABILITY IN ALGORITHMS DESIGN
The partition problem is the task of deciding whether a given set S of positive integers can be partitioned
into two subsets S1 and S2 such that both have the same sum. Although the partition problem is NP-
complete, there is a polynomial time dynamic programming solution, and there are heuristics that solve
the problem either optimally or approximately. For this reason, it has been called “The Easiest Hard
Problem”. Source is Wikipedia. For the partition problem that you need to address in this coursework
the conditions for a 2Part = {Sub1, Sub2
1- Sub1 ∪ Sub2 = S
2- Sub1 ∩ Sub2 = ∅
3- ∑∀a ai i∈ Sub1 = ∑∀b bi i∈Sub2
Example: S = {2,3,4,6,5,10}, 2Part = {{2,3,4,6},{5,10}} or 2Part = {{4,5,6},{2,3,10}}
1- {2,3,4,6} ∪ {5,10} = {2,3,4,6,5,10} = S
2- {2,3,4,6} ∩ {5,10} = {} = ∅
3- 2 + 3 + 4 + 6 = 5 + 10
So the target in this case is specified by one of the subsets sums t = {2 + 3 + 4 + 6} and the other can
be obtained as Sub2=S-Sub1
You are required to write a report about algorithms suitable for tackling the partition problem, defined
above, and investigate their computational complexities in practice by implementing them. You are
provided with the basic C++ classes to help you and you can use any code you prefer.
You report should cover the following:
1. Outline in pseudo-code an Exhaustive Search (Brute Force) solution for the problem (5 marks)
a. Give its time complexity using O-notation
b. Plot the average running time for 10 randomly generated sets S: |S|= 10, 20, 30 and 40
c. Discuss the algorithm complexity in light of your results in b
2. Outline in pseudo-code a Dynamic Programming solution for the problem (5 marks)
a. Give its time complexity using O-notation
b. Plot the average running time for 10 randomly generated sets S: |S|= 10, 20, 30 and 40
c. Discuss the algorithm complexity in light of your results in b
3. Outline in pseudo-code Greedy or Random Sampling approaches to solve this problem(5 marks)
a. Give its time complexity using O-notation
b. Plot the average running time for 10 randomly generated sets S: |S|= 10, 20, 30 and 40
c. Discuss the algorithm complexity in light of your results in b
4. Outline in pseudo-code for Simulated Annealing or Genetic Algorithm for this problem(5 marks)
a. Give its time complexity using O-notation
b. Plot the average running time for 10 randomly generated sets S: |S|= 10, 20, 30 and 40
c. Discuss the algorithm complexity in light of your results in b
5. Write a conclusion with recommendations on when each method is most suitable
a. Reflect on what you have learnt? (3 marks)
b. What could you have done differently? (2 marks)
6. Present your work clearly, using graphs and referencing wherever appropriate (5 marks)
– Please only include pictures for plots of the performance of your! Algorithms