final-review.pdf - Data Structure in Java Final Review Paul Blaer December 6 2017 Weiss Textbook Chapters • Chapter 1(entirely • Chapter 2(entirely

final-review.pdf - Data Structure in Java Final Review Paul...

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Data Structure in Java - Final Review Paul Blaer December 6, 2017 Weiss Textbook Chapters Chapter 1 (entirely) Chapter 2 (entirely) Chapter 3 (entirely) Chapter 4.1, 4.2, 4.3, 4.4, 4.6 (note, 4.6 covers tree traversals the way we covered them in class and makes for good supplemental reading) and 4.8 (note, 4.8 covers java sets and maps, this is supplemental reading only) Chapter 5.1, 5.2, 5.3, 5.4, 5.5, and 5.6 (note, 5.6 covers java hash implementation, this supplemental reading only) Chapter 6.1, 6.2, and 6.3 Chapter 7.1, 7.2, 7.3 (skim only), 7.5, 7.6, 7.7 (you can omit 7.7.6) Chapter 8 - (skim only, just have sense for how unions and finds are relevant to Kruskal’s algorithm) Chapter 9 - 9.1, 9.2, 9.3 (9.3.1 and 9.3.2 and skim 9.3.3), 9.5, 9.7 Material from Outside the Textbook Tower of Hanoi Josephus Problem (this was an example of Queue’s, it’s really not import on its own for the final, but I’m mentioning it here for completeness) Selection Sort (as compared to insertion sort, and as the degenerate of quicksort) 1
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General Concepts Abstract Data Types vs. Data Structures. Recursion. Basic proofs by induction. Java Concepts Basic Java OOP: Classes / Methods / Fields. Visibility modifiers. Generics. Inner classes (static vs. non-static). Interfaces. Iterator / Iterable . Comparable . Analysis of Algorithms Big-O notation for asymptotic running time: O ( f ( n )), Θ( f ( n )), Ω( f ( n )). Typical growth functions for algorithms. Worst case, best case, average case. Skills: Compare growth of functions using big-O notation. Given an algorithm (written in Java), estimate the asymptotic run time (including nested loops and simple recursive calls).
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  • Spring '16
  • Paul Blaer

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