2.2.2 Computational Methods

• can be represented by an Algorithm • solved within a finite timescale • solved at a reasonable cost

• identifying there is a problem to be solved/ what the problem is [1] • stakeholders state what they require from the solution • information is used to clearly define the problem and system requirements • problem may be defined by considering : • strengths and weaknesses of current solution • inputs, outputs, stored data and volume of data

• problems are broken down into smaller problems until each sub problem can be represented as a self contained subroutine • has the same benefits as modular programming

• divide and conquer splits a big problem into smaller parts [1] • repeatedly divides the problem into smaller sub problems halving the size of the problem with every iteration (divide) [1] • each subproblem is solved, often recursively (conquer) • solutions to the sub problems are then recombined (merge) • used in quick sort, merge sort and binary search

• backtracking • data mining • heuristics • performance modelling • pipelining • visualisation

• an approach to finding a solution that explores a possibility until it is no longer deemed feasible. • After finding a solution / failing to find a solution go back to an earlier step to test an alternative • is used in DFS

• involves searching through unconnected data [1] • used to identify patterns or outliers in large sets of data collected from a variety of sources [1] • may include pattern matching algorithms [1] • spots trends or correlations between data which are not immediately obvious [1] • insights from data mining can aid predictions about the future [1] • useful tool in assisting business and marketing decisions [1]

• non optimal, rule of thumb approach to problem solving [1] • which is used when it is unfeasible to analyse all eventualities [1] • provides a good enough solution/result although it is not 100% reliable or accurate [1] • used in A* , machine learning etc

• when the behaviour of something is tested or simulated before it is used in the real world • to identify potential bottlenecks and areas of improvement • can be used for evaluating and predicting the performance characteristics of a software system

• carrying out instructions concurrently [1] • the output of one process is usually an input of the next [1]

• presents data in way that’s easier to understand [1] • makes it possible to identify trends that were not otherwise obvious [1] • eg. Use of graphs, trees, charts etc

• when a node doesn’t have any nodes to visit • the algorithm goes back to the previous visited node • the check for further nodes to visit