At the 16th CHPC & 8th NITheCS Coding Summer School on day 6, Prof Francesco Petruccione and Dipika Ramjugernath presented a session to participants and explored the core strategies that computer scientists use to solve complex computational problems efficiently.
The presenters made a note to participants that not all computational problems are straightforward. Difficulty of problems can grow exponentially when you have an increase of data and naïve approaches become impractical.
Splitting to Solve
To avoid this impracticalities one approach was highlighted in depth divide and conquer, where a large problem is broken into smaller independent parts. A classic example being Binary search, as it reduces the time needed to locate a value in a sorted list to cutting the search space in half repeatedly. Which works similar to merge sort as it efficiently organizes unsorted data by repeatedly sorting and merging halves.
Other Approaches such as Greedy Algorithms were introduced to the participants as a method which makes the best local choice at each step, hoping it leads to a globally optimal solution. To tighten the foundation of the core strategies Dynamic Programming was also introduced to tackle challenges where sub-problems overlap.
It was quite clear 16th CHPC & 8th NITheCS Coding Summer School ensured that the participants are well equipped to have cutting edge systematic approaches to computational problem solving through the service of Prof Francesco Petruccione and Dipika Ramjugernath
