lecture02 - Introduction to Algorithms...

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Introduction to Algorithms 6.046J/18.401J/SMA5503 Lecture 2 Prof. Erik Demaine
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Day 3 Introduction to Algorithms L2.2 Solving recurrences The analysis of merge sort from Lecture 1 required us to solve a recurrence. Recurrences are like solving integrals, differential equations, etc. o Learn a few tricks. Lecture 3 : Applications of recurrences.
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Day 3 Introduction to Algorithms L2.3 Substitution method 1. Guess the form of the solution. 2. Verify by induction. 3. Solve for constants. The most general method: Example: T ( n ) = 4 T ( n /2) + n [Assume that T (1) = Θ (1) .] Guess O ( n 3 ) . (Prove O and separately.) Assume that T ( k ) ck 3 for k < n . Prove T ( n ) cn 3 by induction.
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Day 3 Introduction to Algorithms L2.4 Example of substitution 3 3 3 3 3 ) ) 2 / (( ) 2 / ( ) 2 / ( 4 ) 2 / ( 4 ) ( cn n n c cn n n c n n c n n T n T = + = + + = desired residual whenever ( c /2) n 3 n 0 , for example, if c 2 and n 1 . desired residual
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Day 3 Introduction to Algorithms L2.5 Example (continued) We must also handle the initial conditions, that is, ground the induction with base cases. Base: T ( n ) = Θ (1) for all n < n 0 , where n 0 is a suitable constant. For 1 n < n 0 , we have “ Θ (1) cn 3 , if we pick c big enough. This bound is not tight!
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Day 3 Introduction to Algorithms L2.6 A tighter upper bound? We shall prove that T ( n ) = O ( n 2 ) . Assume that T ( k ) ck 2 for k < n : ) ( 4 ) 2 / ( 4 ) ( 2 n O n cn n n T n T = + + = Wrong! We must prove the I.H. 2 2 ) ( cn n cn = for no choice of c > 0 . Lose! [ desired residual ]
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Day 3 Introduction to Algorithms L2.7 A tighter upper bound! I
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This note was uploaded on 07/09/2009 for the course CSE 6.046J/18. taught by Professor Piotrindykandcharlese.leiserson during the Fall '04 term at MIT.

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lecture02 - Introduction to Algorithms...

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