The University of Michigan Electrical Engineering & Computer Science EECS 281: Data Structures and Algorithms Winter 2018 MIDTERM EXAM Multiple Choice Portion KEY 2 – ANSWERS Wednesday February 21, 2018 6:30PM – 8:00PM (90 minutes) INSTRUCTIONS: • Select only ONE answer per question. • The exam is closed book and notes except for one 8.5”x11” […]
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Lecture 3 – Linked Lists Lecture 24 Knapsack Solved All Ways Shortest Path Algorithms EECS 281: Data Structures & Algorithms http://xkcd.com/287 Many http://xkcd.com/287/ Data Structures & Algorithms The Knapsack Problem 3 Knapsack Problem Defined • A thief robbing a safe finds it filled with N items – Items have various sizes (or weights) – Items
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20_MST.pdf EECS 281: Data Structures & Algorithms Lecture 20 Minimum Spanning Trees Data Structures & Algorithms Minimum Spanning Trees 3 The Minimum Spanning Tree Problem Given: edge-weighted, undirected graph G = (V, E) Find: subgraph T = (V, E’), E’ Í E such that § All vertices are pair-wise connected § The sum of all
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Cryptography – CMSC 414 Cryptography CMSC 414 Goals of Cryptography There are two main goals of cryptography: I Keep secrets secret (Confidentiality, Privacy, Anonymity) I Ensure that data is correct (Integrity, Authenticity) Cryptosystem Principles Kerckhoffs’s Principle: “A cryptosystem should be secure even if everything about the system, except the key, is public knowledge.” Schneier’s Law:
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The University of Michigan Electrical Engineering & Computer Science EECS 281: Data Structures and Algorithms Winter 2015 MIDTERM EXAM, PRACTICE Wednesday February 25, 2015 7:10PM – 8:40PM (90 minutes) Name: Uniqname: Student ID: Discussion section: (circle one) MON 10:30 MON 3:00 MON 5:00 TUE 5:00 WED 9:30 WED 3:00 WED 4:30 THU 5:00 FRI 10:30
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25_Computational_Geometry.pdf Lecture 25 Computational Geometry EECS 281: Data Structures & Algorithms Data Structures & Algorithms Computational Geometry Overview Raster (Bitmap) Graphics Images on screen are represented by pixel arrays Same idea works for storing images in files • RGB: 3 colors per pixel, 1 byte per color • An 1920×1080 image would require ~6.22MB •
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21_Algorithm_Families.pdf Lecture 21 Algorithm Families EECS 281: Data Structures & Algorithms Outline • Brute-Force • Greedy • Divide and Conquer • Dynamic Programming • Backtracking • Branch and Bound 2 Data Structures & Algorithms Brute-Force & Greedy Algorithms Brute-Force Algorithms Definition: Solves a problem in the most simple, direct, or obvious way • Not distinguished
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03_Complexity_Analysis Lecture 3 Complexity Analysis EECS 281: Data Structures & Algorithms Complexity Analysis: Overview Data Structures & Algorithms Complexity Analysis • What is it? – Given an algorithm and input size n, how many steps are needed? – Each step should take O(1) time – As input size grows, how does number of steps change?
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/* * unique_difference.cpp * Univeristy of Michigan, Ann Arbor * EECS 281 W19E1 Q26. * SUBMIT ONLY THIS FILE TO GRADESCOPE. */ // Common #includes for convience. // No need to use them. // Remove any of them if you want. #include #include #include #include #include #include #include #include #include #include #include #include #include #include
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22_Backtracking_BB_TSP.pdf Lecture 22 Backtracking, Branch and Bound Algorithms EECS 281: Data Structures & Algorithms 2 Outline • Review – Constraint Satisfaction – Optimization • Backtracking – General Form – n Queens • Branch and Bound – Traveling salesperson problem Data Structures & Algorithms Backtracking 4 Types of Algorithm Problems • Constraint satisfaction problems – Can
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