Computational Methods (OCR A Level Computer Science)
Revision Note
Features of Computation
It's important to consider if problems can be solved using algorithms and programming code
For example:
Sorting a list of numbers is feasible using algorithms like quick sort or merge sort
But ethical problems or social problems, like determining if a loan should be approved, may incorporate some human input that algorithms could oversimplify or misinterpret
Problems that can be solved with algorithms and programming code are computational problems
Real-world constraints on computable problems
Practical limitations such as computing power, speed, and memory can affect whether a problem is solvable
Running complex machine learning models on a regular laptop might be constrained by limited processing power and memory
Challenges with resource-intensive problems
Some problems are theoretically solvable but not practical due to resource limitations
For example, calculating Pi to a billion decimal places is theoretically possible but impractical due to the amount of computational resources needed
Advances in technology
Technological improvements have expanded the types of problems that can be computationally solved
For example, genome sequencing has become quicker and more affordable due to advances in technology
Example
Below is a table of problems identified for an online grocery business
The table shows which problems are computational and a reason why
Problem Description | Computational Problem | Justification |
Inventory levels are not updated in real-time | Yes | Real-time syncing can be achieved through algorithms |
High rate of employee turnover | No | Root causes are likely cultural or managerial, not algorithmic |
Incorrectly sorted products in the delivery van | Yes | Sorting algorithms can optimise placement for efficiency |
Long wait times for customer service | Yes | Queue algorithms can improve response times |
Poor route optimisation for delivery trucks | Yes | Routing algorithms exist for this specific problem |
Inadequate marketing strategies | No | Marketing strategies often require creative and human-centric solutions |
Worked Example
The table below outlines various challenges in a public transportation system.
Evaluate each problem and decide whether it is computational and justify each of your decisions.
Problem Description | Computational | Justification |
|---|---|---|
Frequent train delays |
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Overcrowding in peak hours |
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Difficulty in finding the shortest route |
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Vandalism in the stations |
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Inconsistent fare pricing |
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Inaccurate timetable |
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How to Answer This Question
Review the Table: Take a moment to read through the problems listed and think about whether a computational solution would be possible
Fill in the Table: In the "computational" column, specify whether the problem should or should not be solved computationally
Justify your answers: In the last column, justify your decision for each problem briefly
Answer:
Problem Description | Computational | Justification |
|---|---|---|
Frequent train delays | Yes | Real-time tracking and predictive algorithms can help in better scheduling |
Overcrowding in peak hours | Yes | Load-balancing algorithms can redistribute passengers or add more trains during peak hours |
Difficulty in finding the shortest route | Yes | Route-finding algorithms can quickly identify the most efficient path |
Vandalism in the stations | No | Requires human intervention such as increased security personnel |
Inconsistent fare pricing | Yes | Dynamic pricing algorithms can ensure fare consistency |
Inaccurate timetable | Yes | Timetabling algorithms can generate more accurate and optimised timetables |
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