2.3.1 Algorithms: revision quiz and key terms
Free practice for 2.3.1 Algorithms in OCR A Level Computer Science (H446). Try the questions, then check the key terms.
Try 5 free questions
What is the worst-case time complexity of a binary search?
Analysing an algorithm involves evaluating how resource requirements grow with input size.
Which condition must be met before a binary search can be used?
Dijkstra's algorithm can be used to find the shortest path in a graph with negative edge weights.
Which sorting algorithm repeatedly splits a list into halves and then combines them back together in order?
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Key terms for 2.3.1 Algorithms
- A* algorithm
- Improves on Dijkstra's using a heuristic to estimate remaining distance.
- Algorithm analysis
- Evaluating how resource requirements grow as input size increases.
- Big O notation
- Describes the upper bound on how resource use grows as input size increases.
- Linear search
- Checks each item in a list one at a time until found or the list ends.
- O(log n)
- Typically associated with methods that repeatedly divide the problem in half.
- O(n squared)
- Typically arises from nested loops, each iterating over the input.
- Time efficiency
- How an algorithm's running time changes as input size increases.
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