'Optimal binary search trees' presentation slideshows

Unit 4: Dynamic Programming

Unit 4: Dynamic Programming. Course contents: Assembly-line scheduling Rod cutting Matrix-chain multiplication Longest common subsequence Optimal binary search trees Applications: optimal polygon triangulation, flip-chip routing, technology mapping for logic synthesis Reading: Chapter 15.

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What we learnt

What we learnt. We learnt what are forests , trees , bintrees , binary search trees . And some operation on the tree i.e. insertion, deletion, traversing. Our new topic. Optimal Binary search Trees. In this chapter there is no insertion no deletion

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Dynamic Programming

Dynamic Programming. Longest Common Subsequence. Problem: Given 2 sequences, X = ? x 1 ,...,x m ? and Y = ? y 1 ,...,y n ? , find a common subsequence whose length is maximum. springtime ncaa tournament basketball printing north carolina krzyzewski

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Chapter 7

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CS 5243: Algorithms

CS 5243: Algorithms. Dynamic Programming. Dynamic Programming. Dynamic Programming is applicable when sub-problems are dependent ! In the case of Divide and Conquer they are independent . Often used in optimization problems. Fib(5). Fib(4). Fib(3). Fib(3). Fib(2). Fib(2). Fib(1).

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MA/CSSE 473 Days 31-32

MA/CSSE 473 Days 31-32. Optimal linked lists Optimal BSTs. MA/CSSE 473 Days 31-32. Student Questions? Optimal Linked Lists Expected Lookup time in a Binary Tree Optimal Binary Tree (intro Day 31, finish Day 32). Warmup : Optimal linked list order.

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INTRODUCTION TO ALOGORITHM DESIGN STRATEGIES

INTRODUCTION TO ALOGORITHM DESIGN STRATEGIES. Subject: design & Analysis of algorithm, CSE-5 th Sem. Prepared By Prof. Saquib Ahmed Anjuman College of Engineering & Technology Department of Computer Science & Engineering. Syllabus.

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