1. All algorithms are exactly the same in terms of their efficiency in solving the same problem (e.g. binary and linear search)
2. Measures are normally expressed as a function of the size of the input n. Common measures include:
3. On a very small list of say two items, it would be more efficient to use the following searching algorithm:
4. If an application is time critical then it may be better to prioritise speed over space
5. A certain program requires the value of every pixel in a stored image to be changed. There are two algorithms available. What statement is true of algorithm #1
6. For maximum algorithm efficiency we wish to minimize resource usage (the resources being …..)
7. The importance of efficiency with respect to time was emphasised by __________ in 1843 as applying to Charles Babbage's mechanical analytical engine
8. Often a task could be handled either by using a fast algorithm which used a lot of memory, or by using a slower algorithm which used very little working memory
9. An algorithm is considered efficient if its resource consumption (or computational cost) is at or below some acceptable level. Roughly speaking, 'acceptable' means:
10. Big O notation is used to represent the complexity of an algorithm as a function of the size of the input n
11. One factor in efficiency is called 'scalability' or complexity. In this context, complexity means 'if input n increases, how much longer does it take to complete' which is…
12. The question:" If input 'n' increases how many more resources does it need" is essentially referring to 'space complexity'.
13. For the following algorithm, which sums up all the numbers up to 'n', the time taken to complete this algorithm ……
14. It is preferrable to have an algorithm which is constant and does not depend on the size of n.
15. Suppose you were required to sort 200 words into alphabetical order. You could do this using brute force going through every combination to find the right order.
16. The more practical way to attempt a sort would be to use a
17. Is it better to run a really fast algorithm that takes up a lot of memory or is it better to run a slower algorithm that uses less memory?
18. If there is a hardware limit to the memory capacity then a slower algorithm may be best for the situation
19. Look at the two algorithms that require the value of every pixel in a stored image to be changed. What is true of algorithm #2?
20. The Big O notation (learn more in advanced levels) basically quantifies how the input 'n' affects the outcome O