Presentation is loading. Please wait.

Presentation is loading. Please wait.

ACM reminders October 30 -- HMC Mock contest Good times ? November 10 -- ACM contest November 13 -- Final acm class & contest wrap-up PendingAzusa Pacific.

Published byCandace Davis Modified over 9 years ago

Similar presentations

Presentation on theme: "ACM reminders October 30 -- HMC Mock contest Good times ? November 10 -- ACM contest November 13 -- Final acm class & contest wrap-up PendingAzusa Pacific."— Presentation transcript:

1 ACM reminders October 30 -- HMC Mock contest Good times ? November 10 -- ACM contest November 13 -- Final acm class & contest wrap-up PendingAzusa Pacific University: 4Alpha2Omega PendingBrigham Young University -- Hawaii Campus: C-Siders 1 PendingBrigham Young University -- Hawaii Campus: C-Siders 2 PendingCalifornia Lutheran University: Java the Hut PendingCalifornia Lutheran University: Just Wanna Program PendingCalifornia Lutheran University: javac this.java PendingCalifornia State University Long Beach: Beach1 PendingCalifornia State University Long Beach: Beach2 PendingCalifornia State University Long Beach: Beach3 PendingCalifornia State University, Northridge: CSUN Red PendingCalifornia State University, Northridge: CSUN Black PendingCalifornia State University, Northridge: CSUN-3 PendingEl Camino College: ECC Beta AcceptedHarvey Mudd College: HMC 1 AcceptedHarvey Mudd College: HMC 2 AcceptedHarvey Mudd College: HMC 3 PendingMount San Antonio College: MtSAC@Voidies$qf PendingMount San Antonio College: MtSAC@Floaties$qi PendingRiverside Community College: Platinum PendingRiverside Community College: The Code Machine PendingRiverside Community College: CodeBlue PendingUniversity of California, San Diego: UCSD Rock PendingUniversity of California, San Diego: UCSD Paper PendingUniversity of California, San Diego: UCSD Scissors PendingUniversity of Southern California: Trojan Horse PendingUniversity of Southern California: Trojan Pride PendingUniversity of Southern California: Trojans current list of competitors

2 C++ Map multimap #include map #include www.dinkumware.com/htm_cpl/index.htmlwww.sgi.com/tech/stl/ set of key/value pairs

3 C++ Map multimap #include map #include www.dinkumware.com/htm_cpl/index.htmlwww.sgi.com/tech/stl/ set of key/value pairs (implemented as a balanced binary tree)

4 C++ Map multimap #include map #include www.dinkumware.com/htm_cpl/index.htmlwww.sgi.com/tech/stl/ set of key/value pairs (implemented as a balanced binary tree) Fast: searching by key Slow: searching by value

5 C++ Map #define MP make_pair typedef pair PII; map m; m[MP(0,1)] = 10; m[MP(0,2)] = 17; m[MP(1,2)] = 5; m[MP(2,0)] = 12; // probably not worth it for graphs... map m; // definitely worth it here m[“ArcOS”] = 110; // as an associative array m[“TheoComp”] = 140; multimap d; // as a dictionary d.insert(MP(“fun”,“c++ coding”)); // methods exist to get d.insert(MP(“fun”,“ACM coding”)); // all of “fun”s entries 0 2 1 10 17 5 12

6 Geometric Problems Problem 1 - Binary Space Partitions observer Basic idea: draw objects from far (first) to near (last).

7 Geometric Problems Problem 1 - Binary Space Partitions observer Basic idea: draw objects from far (first) to near (last). z x (0,-big) (100,200) (100,220) (70,200) (70,220) (70,50) (70,70) (40,50) (40,70) (50,220) (50,240) (20,220) (20,240) (-30,210) (-30,230) (-60,210) (-60,230) (-20,60) (-20,80) (-50,60) (-50,80) A B C D E

8 Geometric Problems Problem 1 - Binary Space Partitions Input: 5 4 -50 60 -20 60 -20 80 -50 80 4 -60 210 -30 210 -30 230 -60 230 4 20 220 50 220 50 240 20 240 4 70 200 100 200 100 220 70 220 4 40 50 70 50 70 70 40 70 first part

9 Geometric Problems Problem 1 - Binary Space Partitions observer Basic idea: draw objects from far (first) to near (last). z x (0,-big) (100,200) (100,220) (70,200) (70,220) (70,50) (70,70) (40,50) (40,70) (50,220) (50,240) (20,220) (20,240) (-30,210) (-30,230) (-60,210) (-60,230) (-20,60) (-20,80) (-50,60) (-50,80) A B C D E (0,140) (-15,0)

10 Geometric Problems Problem 1 - Binary Space Partitions observer Basic idea: draw objects from far (first -- LEFT) to near (last -- RIGHT). z x (0,-big) A B C D E (0,140) (-15,0) ABCDE

11 Geometric Problems Problem 1 - Binary Space Partitions observer Basic idea: draw objects from far (first -- LEFT) to near (last -- RIGHT). z x (0,-big) A B C D E (0,140) (-15,0) ABCDE (70,150) (-70,150) ABCDE

12 Geometric Problems Problem 1 - Binary Space Partitions Input: 5 4 -50 60 -20 60 -20 80 -50 80 4 -60 210 -30 210 -30 230 -60 230 4 20 220 50 220 50 240 20 240 4 70 200 100 200 100 220 70 220 4 40 50 70 50 70 70 40 70 2 0 140 -15 0 70 150 -70 150 first part second part

13 Geometric Problems Problem 1 - Binary Space Partitions Input: 5 4 -50 60 -20 60 -20 80 -50 80 4 -60 210 -30 210 -30 230 -60 230 4 20 220 50 220 50 240 20 240 4 70 200 100 200 100 220 70 220 4 40 50 70 50 70 70 40 70 2 0 140 -15 0 70 150 -70 150 first part second part Output: ECDAB the objects, in the order they would be rendered by this BSP

14 Geometric Problems Problem 2 - Visualizing cubes 3 3 1 3 1 2

15 Geometric Problems Problem 2 - Visualizing cubes 3 3 1 3 1 2 Suppose you rotate so that left wall right wall floor left wallfloor right wall left wall What is the resulting stacking pattern?

16 Geometric Problems Problem 2 - Visualizing cubes 3 3 1 3 1 2 Suppose you rotate so that left wall right wall floor left wallfloor right wall left wall What is the resulting stacking pattern? 3 2 1 2 1 1 2 1

17 Geometric Problems Problem 2 - Visualizing cubes ?!?

18 All-pairs shortest paths A B E D C 8 13 1 6 12 9 7 0 11 0813-1 -0-612 -90-- 7-00- ---110 A B C D E A BC DE from to “Floyd-Warshall algorithm”

19 All-pairs shortest paths A B E D C 8 13 1 6 12 9 7 0 11 0813-1 -0-612 -90-- 7-00- ---110 A B C D E A BC DE from to “Floyd-Warshall algorithm” D 0 = (d ij ) 0 d ij = shortest distance from i to j through nodes {1, …, k} k d ij = shortest distance from i to j through no nodes 0

20 All-pairs shortest paths A B E D C 8 13 1 6 12 9 7 0 11 0813-1 -0-612 -90-- 7-00- ---110 A B C D E A BC DE from to “Floyd-Warshall algorithm” D 0 = (d ij ) 0 d ij = k d ij = shortest distance from i to j through nodes {1} 1

21 All-pairs shortest paths... 0813-1 -0-612 -90-- 7-00- ---110 A B C D E D 0 = (d ij ) 0 0813-1 -0-612 -90-- 7-00- ---110 A B C D E D 1 = (d ij ) 1 d ij = shortest distance from i to j through nodes {1, …, k} k d ij = k 0813-1 -0-612 -90-- 7 15 00 8 ---110 A B C D E “Floyd-Warshall algorithm”

22 All-pairs shortest paths... 0813-1 -0-612 -90-- 7-00- ---110 A B C D E D 0 = (d ij ) 0 0813-1 -0-612 -90-- 7-00- ---110 A B C D E D 1 = (d ij ) 1 d ij = shortest distance from i to j through {1, …, k} k d ij = k 0813-1 -0-612 -90-- 7 15 00 8 ---110 A B C D E “Floyd-Warshall algorithm”

23 All-pairs shortest paths... 0813-1 -0-612 -90-- 7-00- ---110 A B C D E D 0 = (d ij ) 0 0813-1 -0-612 -90-- 7-00- ---110 A B C D E D 1 = (d ij ) 1 d ij = shortest distance from i to j through {1, …, k} k d ij = k 0813-1 -0-612 -90-- 7 15 00 8 ---110 A B C D E “Floyd-Warshall algorithm”

24 All-pairs shortest paths... 0813 14 1 -0-612 -901521 715008 ---110 A B C D E D 2 = (d ij ) 2 0813141 -0-612 -901521 7 9 008 ---110 A B C D E D 3 = (d ij ) 3 0813141 130 6 612 22901521 79008 182011 0 A B C D E D 4 = (d ij ) 4 A B C D E D 5 = (d ij ) 5 to store the path, another matrix can track the last intermediate vertex 0812 1 1306612 22901521 79008 182011 0

Download ppt "ACM reminders October 30 -- HMC Mock contest Good times ? November 10 -- ACM contest November 13 -- Final acm class & contest wrap-up PendingAzusa Pacific."

Similar presentations

David Luebke 1 6/7/2014 CS 332: Algorithms Skip Lists Introduction to Hashing.

David Luebke 1 6/7/2014 CS 332: Algorithms Skip Lists Introduction to Hashing.
Jotto! JRsSRsElderly slate 3 This term's first class to guess another's word earns 1 problem... slate 2slate 1 This term's last class to have its word.

Jotto! JRsSRsElderly slate 3 This term's first class to guess another's word earns 1 problem... slate 2slate 1 This term's last class to have its word.
CSE 390B: Graph Algorithms Based on CSE 373 slides by Jessica Miller, Ruth Anderson 1.

CSE 390B: Graph Algorithms Based on CSE 373 slides by Jessica Miller, Ruth Anderson 1.
ACS 7101/3 – Advanced Algorithm Design November 2008 Finding the maximum matching of a bipartite graph Final Project.

ACS 7101/3 – Advanced Algorithm Design November 2008 Finding the maximum matching of a bipartite graph Final Project.
The Dictionary ADT Definition A dictionary is an ordered or unordered list of key-element pairs, where keys are used to locate elements in the list. Example:

The Dictionary ADT Definition A dictionary is an ordered or unordered list of key-element pairs, where keys are used to locate elements in the list. Example:
IKI 10100: Data Structures & Algorithms Ruli Manurung (acknowledgments to Denny & Ade Azurat) 1 Fasilkom UI Ruli Manurung (Fasilkom UI)IKI10100: Lecture10.

IKI 10100: Data Structures & Algorithms Ruli Manurung (acknowledgments to Denny & Ade Azurat) 1 Fasilkom UI Ruli Manurung (Fasilkom UI)IKI10100: Lecture10.
Reachability as Transitive Closure

Reachability as Transitive Closure
Design and Analysis of Algorithms Single-source shortest paths, all-pairs shortest paths Haidong Xue Summer 2012, at GSU.

Design and Analysis of Algorithms Single-source shortest paths, all-pairs shortest paths Haidong Xue Summer 2012, at GSU.
Data Structure and Algorithms (BCS 1223) GRAPH. Introduction of Graph A graph G consists of two things: 1.A set V of elements called nodes(or points or.

Data Structure and Algorithms (BCS 1223) GRAPH. Introduction of Graph A graph G consists of two things: 1.A set V of elements called nodes(or points or.
 Graph Graph  Types of Graphs Types of Graphs  Data Structures to Store Graphs Data Structures to Store Graphs  Graph Definitions Graph Definitions.

 Graph Graph  Types of Graphs Types of Graphs  Data Structures to Store Graphs Data Structures to Store Graphs  Graph Definitions Graph Definitions.
 2004 SDU Lecture11- All-pairs shortest paths. Dynamic programming Comparing to divide-and-conquer 1.Both partition the problem into sub-problems 2.Divide-and-conquer.

 2004 SDU Lecture11- All-pairs shortest paths. Dynamic programming Comparing to divide-and-conquer 1.Both partition the problem into sub-problems 2.Divide-and-conquer.
1 Representing Graphs. 2 Adjacency Matrix Suppose we have a graph G with n nodes. The adjacency matrix is the n x n matrix A=[a ij ] with: a ij = 1 if.

1 Representing Graphs. 2 Adjacency Matrix Suppose we have a graph G with n nodes. The adjacency matrix is the n x n matrix A=[a ij ] with: a ij = 1 if.
All Pairs Shortest Paths and Floyd-Warshall Algorithm CLRS 25.2

All Pairs Shortest Paths and Floyd-Warshall Algorithm CLRS 25.2
Midterm 2 Overview Fawzi Emad Chau-Wen Tseng Department of Computer Science University of Maryland, College Park.

Midterm 2 Overview Fawzi Emad Chau-Wen Tseng Department of Computer Science University of Maryland, College Park.
Floyd’s Algorithm (shortest-path problem) Section 8.2.

Floyd’s Algorithm (shortest-path problem) Section 8.2.
Chapter 9 Graph algorithms. Sample Graph Problems Path problems. Connectedness problems. Spanning tree problems.

Chapter 9 Graph algorithms. Sample Graph Problems Path problems. Connectedness problems. Spanning tree problems.
Shortest Paths Definitions Single Source Algorithms –Bellman Ford –DAG shortest path algorithm –Dijkstra All Pairs Algorithms –Using Single Source Algorithms.

Shortest Paths Definitions Single Source Algorithms –Bellman Ford –DAG shortest path algorithm –Dijkstra All Pairs Algorithms –Using Single Source Algorithms.
25.All-Pairs Shortest Paths Hsu, Lih-Hsing. Computer Theory Lab. Chapter 25P.2.

25.All-Pairs Shortest Paths Hsu, Lih-Hsing. Computer Theory Lab. Chapter 25P.2.
CS 280 Data Structures Professor John Peterson. Invariants Back to Invariants! Recall the insertion sort invariant – how can we turn this into debugging.

CS 280 Data Structures Professor John Peterson. Invariants Back to Invariants! Recall the insertion sort invariant – how can we turn this into debugging.
Design and Analysis of Algorithms - Chapter 81 Dynamic Programming Dynamic Programming is a general algorithm design technique Dynamic Programming is a.

Design and Analysis of Algorithms - Chapter 81 Dynamic Programming Dynamic Programming is a general algorithm design technique Dynamic Programming is a.

Similar presentations

Ads by Google