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Algorithm Design and Analysis

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leeroberts
11 diapositives · 184 Views · Jan 03, 2020

Learn to design correct & efficient algorithms, analyze complexity, study well-known algorithms, & understand data structures. Exam includes lab sessions, assignments, & optional projects.

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Algorithm Design and Analysis
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  1. Algorithm Design and Analysis 4th Semester Computer Engineering Spring 2017 Conf.dr.ing. Ioana Ṣora ioana.sora@cs.upt.ro http://staff.cs.upt.ro/~ioana/algo/

  2. Our goal: designalgorithms that are correct andefficient

  3. Analyzing Algorithms • We need methods and metrics to analyze algorithms for: • Correctness • Methods for proving correctness • Efficiency • Time complexity, Asymptotic analysis

  4. Designing Algorithms • Ok, so you will know (will learn) how to analyze a given algorithm. • But where do these algorithms come from ? • Clever people already designed a plethora of solutions (algorithms) for different problems and we find them in textbooks, internet, etc. • But how will you design solutions for new problems ?

  5. How to learn algorithms ? http://www.neatorama.com/twaggies/2010/11/07/no-112-robcorddry/

  6. Kruskal’s algorithm Prim’s algorithm Design methods: • Backtracking = Forbidden  • Greedy • Design by Induction • Divide and Conquer • Dynamic Programming Floyd’s algorithm Dijkstra’s algorithm Tarjan’s algorithm

  7. What about data structures ? • Fundamental data structures: • Model fundamental data • Examples: lists, queues, stacks, hashtables, etc • In this class you can make use of libraries providing implementations of these (such as: Java Collections, C++ STL Containers) • Special data structures: • Created to optimize a specific (set of) operation(s) for a specific context of a specific class of algorithms • Examples: Search trees, balanced trees, optimal trees, disjoint sets, etc

  8. Course Goals • Learn to design algorithms that are correct and efficient • How do we know that: • an algorithm is correct ? -> correctness proofs • an algorithm is efficient ? -> analysis of algorithms (time complexity) • How to design solutions for new problems ? • Learning general techniques for design • Studying a set of well-known algorithms, to serve as examples of success stories for applying general design techniques • Become aware that good algorithms are key parts of software engineering practice !

  9. Examination and Grading • Final grade = 2/3 final written exam + 1/3 work during the semester • Written exam: • Short questions and Algorithm design problems • Work during the semester: • Each lecture topic is followed (next week) by an associated lab session. Have to come prepared to the lab ! • Lab class activity, including quizzes - weight: 40 % • Lab assignments, including homeworks - weight: 50 % • Tool projects – optional – weight: 10 % • Optional award points: for exceptional activity = doing more than one optional tool project during the semester, you can gain extra-points to the grade of your final written exam

  10. Textbooks [Manber] [Unlocked] [McCormick] [CLRS]

  11. Course Webpage All official information related to the Algorithm Design and Analysis classes: http://staff.cs.upt.ro/~ioana/algo/