2.2.2 computational methods

• definable inputs and outputs • finite = limited to a number of steps, not unending • no ambiguity, it has precise instructions and questions • decomposability = be able to be broken down into sub-problems

the process of identifying and defining a problem clearly before trying to design a computational solution

• understanding the needs to be solved • why it’s a problem • whether it can be solved computationally

• prevents wasting time solving the wrong problem • ensures problem is well-defined and measurable • helps determine if a computational method is suitable • guides choice of algorithm, data and resources

• understand problem in context = who are the users, what outcomes are expected • identify the inputs and outputs • define the problem clearly • recognise the constraints and requirements (hardware, software, storage, cost) • decompose

the process of breaking a large, complex problem into smaller, more manageable subproblems that are easier to understand, solve and test

like decomposition = split big problem into smaller pieces to be separately solved and solve

• simplifies complex systems • makes development and debugging easier • allows teamwork • encourages reusability in functions and moduels • improves maintenance

the process of removing unnecessary details and focusing on the essential information needed to solve a problem

• simplifies complex systems • helps focus of relevant data and processes • makes problems easier to understand, design and. model • reduces errors by eliminating irrelevant details

a systematic trial and error algorithmic technique used to find a solution by exploring possible solutions and backtracking when it doesn’t work

+finds all possible solutions +gaurantees a correct solution -can be slow for large problems -may require a lot of memory -won’t be the optimal solution

the process of examine large data sets to discover patterns, trends and useful information from users

• identifying relationships and correlations between data • predicting future trends • supporting computational decisions • detecting unusual patterns

+helps making data-driven decisions +can increase profits + improves user personalisation -data privacy concerns -cost

when you make an educated guess to find a good-enough solution more quickly that finding the optimal solution

+much faster +can be improved over time +useful for large-scale problems -not guaranteed the best solutions -can introduce bias

the process of testing and predicting how well a system performs under different conditions before it is built or deployed

• predict how a system will behave • compare alternative designs • find bottlenecks • ensure it meets the performance requirements • save time and cost

+Identifies issues early +saves time and cost +improves reliability -models only accurate as assumptions and data -may not match real-world conditions

a CPU technique that allows multiple instructions to be processed at different stages simultaneously

• increase instruction throughput • use CPU components efficiently • reduce idle time

+increased throughput +efficient use of CPU +improved performance -data hazards, control hazards, structural hazards

the process of representing data, processing or problems visually so they are easier to understand, analyse and solve