Richard Anderson Lecture 12 Recurrences and Divide and Conquer

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Presentation transcript:

Richard Anderson Lecture 12 Recurrences and Divide and Conquer CSE 421 Algorithms Richard Anderson Lecture 12 Recurrences and Divide and Conquer

Divide and Conquer

Recurrence Examples T(n) = 2 T(n/2) + cn T(n) = T(n/2) + cn O(n log n) T(n) = T(n/2) + cn O(n) More useful facts: logkn = log2n / log2k k log n = n log k

T(n) = aT(n/b) + f(n)

Recursive Matrix Multiplication Multiply 2 x 2 Matrices: | r s | | a b| |e g| | t u| | c d| | f h| r = ae + bf s = ag + bh t = ce + df u = cg + dh A N x N matrix can be viewed as a 2 x 2 matrix with entries that are (N/2) x (N/2) matrices. The recursive matrix multiplication algorithm recursively multiplies the (N/2) x (N/2) matrices and combines them using the equations for multiplying 2 x 2 matrices =

Recursive Matrix Multiplication How many recursive calls are made at each level? How much work in combining the results? What is the recurrence?

What is the run time for the recursive Matrix Multiplication Algorithm? Recurrence:

T(n) = 4T(n/2) + cn

T(n) = 2T(n/2) + n2

T(n) = 2T(n/2) + n1/2

Recurrences Three basic behaviors Dominated by initial case Dominated by base case All cases equal – we care about the depth

Solve by unrolling T(n) = n + 5T(n/2) Answer: nlog(5/2)

What you really need to know about recurrences Work per level changes geometrically with the level Geometrically increasing (x > 1) The bottom level wins Geometrically decreasing (x < 1) The top level wins Balanced (x = 1) Equal contribution

Classify the following recurrences (Increasing, Decreasing, Balanced) T(n) = n + 5T(n/8) T(n) = n + 9T(n/8) T(n) = n2 + 4T(n/2) T(n) = n3 + 7T(n/2) T(n) = n1/2 + 3T(n/4)

Strassen’s Algorithm Multiply 2 x 2 Matrices: | r s | | a b| |e g| | t u| | c d| | f h| Where: p1 = (b + d)(f + g) p2= (c + d)e p3= a(g – h) p4= d(f – e) p5= (a – b)h p6= (c – d)(e + g) p7= (b – d)(f + h) = r = p1 + p4 – p5 + p7 s = p3 + p5 t = p2 + p5 u = p1 + p3 – p2 + p7

Recurrence for Strassen’s Algorithms T(n) = 7 T(n/2) + cn2 What is the runtime?

BFPRT Recurrence T(n) <= T(3n/4) + T(n/5) + 20 n What bound do you expect?