Computer Algorithms II Lecture Notes

28 October 2008 • Greedy Algorithms

Outline

• A scheduling problem.

• Exhaustive search.

• Greedy algorithms.

• Greedy algorithm design.

• Greed advantages and disadvantages.

• Greedy conditions

A Scheduling Problem

• Given an auditorium and a set of presentations, schedule the maximum number of presentations possible.

  • Each presentation has a start and end time.

  • Each presentation requires exclusive use of the auditorium.

  

Exhaustive Search

• Look at every possible schedule.

schedule(presentations)

  schedlue = { }

  for i = 1 to 2size(presentations) - 1
    s = get-schedue(i, presentations)
    if valid(s) ∧ size(s) > size(schedule)
      schedule = s

  return schedule

Faster Scheduling

• Exhaustive search works if you don't schedule a lot of presentations often.

• But what if you do?

• Is there an alternative scheduling algoritm faster than exhaustive search?

An Alterntive Scheduler

1. Order the presentations by increasing finish time.

2. Scan the ordered presentations by increasing finish time.

3. Always schedule the next earliest finishing non-conflicting presentation.

Does It Work?

• Is the schedule S maximal?

• Suppose schedule S' has more presentations.

• Consider the first presentation in S and S'.

  

  • 1 in S must end no later than 1' in S'.

• Replace 1' in S' with 1.

Does It Work??

• Schedules S and S' both agree on the first presentation.

  • Remove it from both schedules.

• The remaining schedules must still be maximal.

  • Otherwise the original schedules weren't maximal.

• Repeat the argument with the newly dimished schedules.

Does It Work???

• The two schedules always agree on the first presentation.

• The remaining schedules are always maximal.

• The two schedules must have the same number of elements.

  • Or perhaps the second schedule wasn't maximal.

• Apparently the new scheduling algorithm works.

Greedy Algorithms

• A greedy algorithm always makes the next available best choice.

greed(p)
  solution = { }
  while choices-left(p)
    solution ∪= best-choice(p)
  return solution

• Problem: Schedule as many presentations as possible.

• Solution: Schedule the earliest finishing non-conflicting presentation.

Greedy Algorithm Design

• Got a problem? Solve it with greed!

  • I wish I had some way of ordering the choices by importance.

  • I wish I had some way of making the next choice.

  • I wish I knew if greed worked in on this problem.

Example

• Assume a non-empty presentation list ordered by increasing finish times.

schedule(p[])

  schedule = { 1 }
  last = 1

  for next = 2 to p.size()
    if p[last].end ≤ p[next].start
      schedule ∪= { next }
      last = next

  return schedule

Greed Advantages

• Always taking the best available choice is usually easy.

  • It usually requires sorting the choices.

• Repeatedly taking the next available best choice is usually linear work.

  • But don't forget the cost of sorting the choices.

• Much cheaper than exhaustive search.

  • Much cheaper than most other algorithms.

But...

• Create a schedule with maximum auditorium use.

• Greed repeatedly schedules the next largest non-conflicting presentation.

• Greed fails:

  

  • Greed schedules 1, but { 2, 3 } is better.

Greed Disadvantages

• Greedy algorithms don't work for some problems.

• Despite their simplicty, correct greedy algorithms can be subtle.

  • It's easy to fool yourself into believing an incorrect greedy algorithm is correct.

  • “I can't think of a counter-example, so there are none.”

• Using greed is not an automatic.

Greedy Problems

• Why do greedy algorithms fail?

  

• Locally optimal choices may not be globally optimal.

Greedy Conditions

• There's no guarenteed way to recognize problems that can be solved by a greedy algorithm.

• But, a problem in which

  1. a locally optimal choice leads to a global optimum, and

  2. each remaining subproblem also leads to an optimal choice

  can be solved with a greedy algorithm.

Summary

• Greedy algorithms can be a fast, simple replacement for exhaustive search algorithms.

  • Sometimes, which is the tricky part.

• Greedy algorithms require optimal local choices.

• If locally optimal choices lead to a global optimum and the subproblems are optimal, then greed works.

  • And some other times too.

References

• Greedy Algorithms, Chapter 17 in Introduction to Algorithms by Thomas Cormen, Charles Leiserson, and Ronald Rivest, MIT Press, 1999.

• Greedy Algorithms, Chapter 5 in Algorithms by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vizirani, McGraw-Hill, 2008

This page last modified on 26 October 2008.