ICS 353: Design and Analysis of Algorithms

1. King Fahd University of Petroleum & Minerals Information & Computer Science Department ICS 353: Design and Analysis of Algorithms Solving Recurrence Relations

2. Reading Assignment • Chapter 2 as a whole, but in particular Section 2.8 • Chapter 4 from Cormen’s Book.

3. Outline • Objective: To discuss techniques for solving recurrence relations. • These techniques will be very important and “handy” tools for analyzing algorithms that are recursive. • Linear Homogenous Recurrences • Characteristic Equations • Inhomogeneous Recurrences • The Master Method • Recurrence Expansion • The Change of Variable Method

4. Linear Homogeneous Equations • Definition: A recurrence relation is called linear homogeneous with constant coefficients if it is of the form: • We restrict our discussion to homogeneous recurrence equations with k=1 or k=2

5. Solution of Linear Homogeneous Equations • When k=1, and hence the solution is • When k=2, the following steps are followed to solve the recurrence: • Find r1 and r2, the solutions to the characteristic equation • If r1 = r2 = r, then Otherwise • Determine c1 and c2 from the initial values f(n0) and f(n0+1)

6. Examples

7. Inhomogeneous Equations • Definition: A recurrence relation is called inhomogeneous if it is not a homogeneous recurrence relation. In particular, we will look at where i 1  i  k  gi(n) is not a constant or g0(n)  0.

8. Solution for Inhomogeneous Recurrences • No general method for solving inhomogeneous recurrences exists. However, • There are cases where a formula for a class of inhomogeneous recurrence relations exists (Master Theorem) • The rest depend on experience and/or trial and error in choosing one of the following techniques • Expansion • Substitution • Change of variable

9. Master Theorem • Theorem: Let a1 and b>1 be constants, let g(n) be a function, and let f(n) be defined on the nonnegative integers by the recurrence where we interpret n/b to mean either n/b or n/b. Then f(n) can be bounded asymptotically as follows • If >0  g(n)=O(nlogba - ), then f(n) = (nlogba). • If g(n)= (nlogba), then f(n) = (nlogba log n). • If >0  g(n)=(nlogba + ), and if c<1, n0  ag(n/b)  cg(n) n > n0, then f(n) = (g(n)).

10. Examples

11. Gaps in the Master Theorem • The three cases in the theorem do not cover all the possibilities for g(n). There are gaps between cases 1 and 2, and 2 and 3. • For example, can we apply the Master theorem on the following recurrence?

12. The Expansion Method • When expanding few terms of some recurrences, a solution may become more apparent. • Example: Consider the previous recurrence

13. The Change of Variables Method • The idea is to change the domain of the function and define a new recurrence in the new domain whose solution may be easier to obtain or is already known. Once the solution is obtained, we convert the domain of the function back to its original domain.

14. Example