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4.3 Fundamentals of algorithms

5 dot points across 5 inquiry questions. Click any dot point for a focused answer with worked past exam questions where available.

How do we measure and compare the efficiency of algorithms?

Understand Big-O notation for time and space complexity, the common orders of growth, how to determine the complexity of an algorithm, and the meaning of best, average and worst case.
A focused answer to AQA A-Level Computer Science 4.3.5, covering Big-O notation for time and space complexity, the common orders of growth, determining the complexity of an algorithm, and best, average and worst case.
8 min answer →

How does Dijkstra's algorithm find the shortest path in a weighted graph?

Understand Dijkstra's shortest path algorithm, how it finds the lowest-cost path from a start vertex, the role of a priority queue, and its applications.
A focused answer to AQA A-Level Computer Science 4.3.4, covering Dijkstra's shortest path algorithm, how it finds the lowest-cost path from a start vertex in a weighted graph, the role of a priority queue, and its applications.
8 min answer →

How are graphs and trees systematically traversed?

Understand depth-first and breadth-first graph traversal, the data structures they use, and the pre-order, in-order and post-order tree traversal algorithms.
A focused answer to AQA A-Level Computer Science 4.3.1, covering depth-first and breadth-first graph traversal and the data structures they use, plus the pre-order, in-order and post-order tree traversal algorithms.
8 min answer →

How do linear and binary search work, and which is faster?

Understand the linear search and binary search algorithms, how each works, their requirements, and their time complexity.
A focused answer to AQA A-Level Computer Science 4.3.2, covering the linear search and binary search algorithms, how each works, the requirement that binary search needs sorted data, and their time complexity.
8 min answer →

How do bubble sort and merge sort work, and which is more efficient?

Understand the bubble sort and merge sort algorithms, how each orders a list, their time complexity, and the trade-off between them.
A focused answer to AQA A-Level Computer Science 4.3.3, covering the bubble sort and merge sort algorithms, how each works, their time complexity, and the trade-off between simplicity and efficiency.
8 min answer →