Class 20: Algorithm Analysis (1)

Held: Monday, 23 February 2004

Summary: Today we consider techniques for comparatively evaluating the running time of algorithms.

Related Pages:

• EBoard.
• timing-sum.scm: Some sample code.

Notes:

• Some of you asked about the relationship between tail recursion and iteration. We'll discuss it in class today.
• Are there questions on homework 4?
• An optional reading on higher-order procedures should be available outside my office this afternoon.

Overview:

• Comparing Algorithms.
• Asymptotic Analysis.
• Eliminating Constants.
• Asymptotic Analysis in Practice.
• The Role of Details.

Comparing Algorithms

• As you may have noted, there are often multiple algorithms one can use to solve the same problem.
  • In finding the minimum element of a list, you can step through the list, keeping track of the current minimum. You could also sort the list and grab the first element.
  • In finding x^y, one might use repeated multiplication, divide and conquer, or even the built-in eⁿ and natural log procedures.
  • You can come up with your own variants.
• How do we choose which algorithm is the best?
  • The fastest/most efficient algorithm.
  • The one that uses the fewest resources.
  • The clearest.
  • The shortest.
  • The easiest to write.
  • The most general.
  • ...
• Frequently, we look at the speed. That is, we consider how long the algorithm takes to run.
  • It is therefore important for us to be able to analyze the running time of our algorithms.

Difficulties Analyzing Running Times

• Is there an exact number we can provide for the running time of an algorithm?
  • Surprisingly, no.
• Different inputs lead to different running times. For example, if there are conditionals in the algorithm (as there are in many algorithms), different instructions will be executed depending on the input.
• Not all operations take the same time. For example, addition is typically quicker than multiplication, and integer addition is typically quicker than floating point addition.
• The same operation make take different times on different machines.
• The same operation may appear to take different times on the same machine, particularly if other things are happening on the same machine.
• Many things that affect running time happen behind the scenes and cannot be easily predicted. For example, the computer might move some frequently-used data to cache memory.

Asymptotic Analysis

• Noting problems in providing a precise analysis of the running time of programs, computer scientists developed a technique which is often called asymptotic analysis. In asymptotic analysis of algorithms, one describes the general behavior of algorithms in terms of the size of input, but without delving into precise details.
  • The analysis is asymptotic in that we look at the behavior as the input gets larger.
• There are many issues to consider in analyzing the asymptotic behavior of a program. One particularly useful metric is an upper bound on the running time of an algorithm. We call this the Big-O of an algorithm.
• Big-O is defined somewhat mathematically, as a relationship between functions.
• f(n) is in O(g(n)) iff
  • there exists a number n₀
  • there exists a number d > 0
  • for all n > n₀, abs(f(n)) <= abs(d*g(n))
• What does this say? It says that after a certain value, n₀, f(n) is bounded above by a constant (that is, d) times g(n).
  • The constant, d, helps accommodate the variation in the algorithm.
  • We don't usually identify the d precisely.
  • The n₀ says for big enough n.
• We can apply big-O to algorithms.
  • n is the size of the input (e.g., the number of items in a list or vector to be manipulated).
  • f(n) is the running time of the algorithm.
• Some common Big-O bounds
  • An algorithm that is in O(1) takes constant time. That is, the running time is independent of the input. Getting the size of a vector should be an O(1) algorithm.
  • An algorithm that is in O(n) takes time linear in the size of the input. That is, we basically do constant work for each element of the input. Finding the smallest element in a list is often an O(n) algorithm.
  • An algorithm that is in O(log₂(n)) takes logarithmic time. While the running time is dependent on the size of the input, it is clear that not every element of the input is processed. Many such algorithms involve the strategy of divide and conquer.

Eliminating Constants

• One of the nice things about asymptotic analysis is that it makes constants unimportant because they can be hidden in the d.
• If f(n) is 100*n seconds and g(n) is 0.5*n seconds, then
  • f(n) is in O(g(n)) [let d be 200]
  • g(n) is in f(n) [let d be 1]
• If f(n) is 100*n seconds and g(n) is n*n seconds, then f(n) is in O(g(n)) [let n₀ be 100 and d be 1]
• However, g(n) is not in O(f(n)). Why not?
  • Suppose there were an n₀ and a d.
  • Consider what happens for n = 101*d.
  • d*f(n) = d*100*101*d = d*d*100*101.
  • However, g(n) = d*d*101*101, which is even larger.
  • If n₀ is greater than 101d, we'll still have this problem [proof left to reader].
• Since constants can be eliminated, we normally don't write them.
  • That is, we say that the running time of an algorithm is O(n) or O(n²) or ....

Asymptotic Analysis in Practice

• We now have a theoretical grounding for asymptotic analysis. How do we do it in practice?
• At this point in your career, it's often best to count the steps in an algorithm and then add them up. After you've taken combinatorics, you can use recurrence relations.
• Over the next few days, we'll look at a number of examples. Some starting ones.
  • Finding the smallest/largest element in a vector of integers.
  • Finding the average of all the elements in a vector of integers.
  • Putting the largest element in an array at the end of the array. if we're only allowed to swap subsequent elements.
  • Computing the nth Fibonacci number.
  • ...

The Role of Details

• Although the previous discussion may make it seem like precise details of algorithms aren't important, some details are particularly important.