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Bowling Green | CS 4520

#### 24 sample documents related to CS 4520

• Bowling Green CS 4520
/Setting web account: ftp ftp.student.gsu.edu login *login* password mkdir public_html chmod 755 public_html cd public_html put yourcode.zip bye /Checking if you did it right http:/www.student.gsu.edu/~*login*/yourcode.zip

• Bowling Green CS 4520
n=1000 m=8544 0 25 244 108 275 140 273 159 313 219 199 254 392 369 171 518 271 538 250 568 253 603 307 613 196 638 314 1 24 187 43 182 65 331 155

• Bowling Green CS 4520
Steiner Tree Problem Given: A set S of points in the plane = terminals Find: Minimum-cost tree spanning S = minimum Steiner tree Euclidean metric Terminals 1 1 Cost = 2 1 1 Cost = 3 Steiner Point 1 Rectilinear metric 1 Cost = 6 1 1 1 1 1 Cos

• Bowling Green CS 4520
Polynomials and Fast Fourier Transform Chapter 30, pp.823-848 new edition 10/16/01 - 10/18/01 CS8550 Polynomials Polynomial in coefficient representation A(x ) = a0 + a1x +a2 x2 +.+ an-2 xn-2 + an-1 xn-1 Operations over polynomials: polynom

• Bowling Green CS 4520
Political problem Suppose a politician trying to win an election 3 types of areas - urban, suburban, rural. Certain issues - road, gun control, farm subsidies, gasoline tax. Try to find out the minimum amount of money you need to win 50,000 urban, 10

• Bowling Green CS 4520
Hamiltonian Cycle and TSP Hamiltonian Cycle: given an undirected graph G find a tour which visits each point exactly once Traveling Salesperson Problem given a positive weighted undirected graph G (with triangle inequality = can make shortcuts

• Bowling Green CS 4520
Single-Source Shortest Paths (25/24) HW: 25-2 and 25-3 p. 546/24-2 and 24-3 p.615 Given a graph G=(V,E) and w: E weight of <v[1],.,v[k]> is w(p) = w(v[i],v[i+1]) Single-source shortest-paths: find a shortest path from a source s to every vert

• Bowling Green CS 4520
Greedy Algorithm (17.4/16.4) Greedy Algorithm (GA) always makes choice which is the best at the moment does not look ahead: local very powerful in practice: simplest and fastest in general, does not return the optimal solution convex = each

• Bowling Green CS 4520
Insertion Sort 7 5 6 9 9 6 7 6 7 9 9 15 16 10 15 5 11 16 15 10 15 16 11 16 Divide and Conquer (1.3/2.3) Divide (into two equal parts) Conquer (solve for each part separately) Combine separate solutions Merge sort Divide into two equal p

• Bowling Green CS 4520
n=25000 m=576014 0 176 67 665 185 1129 26 1414 114 1748 205 2027 264 2714 212 2743 132 3105 275 3181 273 3836 317 3971 412 4116 84 4139 216 4552 183 4860 99 5081

• Bowling Green CS 4520
Sorting OVERVIEW 1-st Midterm: 3 problems Data structures Quicksort Greedy algorithm binary heaps BST Dynamic Programming Shortest Paths Prims algorithm Longest Common subsequence shortest triangulation Dijkstra Bellman-ford Ma

• Bowling Green CS 4520
Analysis and Design of Algorithms An algorithm is a method of solving problem (on a computer) Problem example: given a set of points on the plane find the closest pair Algorithm: find distance between all pairs Can we do it faster? Combinat

• Bowling Green CS 4520
Randomized Quicksort (8.4.2/7.4.2) Randomized Quicksort i = Random(p, r) swap A[p] A[i] partition A(p, r) Average analysis = Expected runtime 1 n -1 2 n -1 T (n) = [T (k ) + T (n - k )] + (n) = T (k ) + (n) n k =1 n k =1 solving recurrence

• Bowling Green CS 4520
Dynamic Programming (16.0/15) The 3-d Paradigm 1st = Divide and Conquer 2nd = Greedy Algorithm Dynamic Programming = metatechnique (not a particular algorithm) Programming = `tableau method\', not writing a code Longest Common Subsequence (16.3/

• Bowling Green CS 4520
Greedy Algorithm to Find Minimum Length Triangulation CSC6520 Algorithm Graduate Student Project Instructor : Dr. Alexander Zelikovsky Student : Yan Liu Spring 2001 04/21/09 1 Introduction Definition of Triangulation - a set T of chords of a c

• Bowling Green CS 4520
Computational Geometry (35/33) Line Segments and cross-product Segment intersection and Sweep Line Convex Hull and Grahams Scan, Jarviss march Divide-and-Conquer for Closest Pair. A segment is a convex combination of ends p1 = (x1,y1) and p2 =

• Bowling Green CS 4520
NP-Completeness (36.4-5/34.4-5) P: yes and no in pt NP: yes in pt NPH NPC NPC P NP NP-hard Satisfiability Boolean formulas: x, (x y) (x y) (xy) (xy) Satisfiability Problem (SP): given a Boolean formula is there any 0-1 input (0-1 assignment

• Bowling Green CS 4520
Selecting Forwarding Neighbors in Wireless Ad Hoc Networks Yuchen Wang Ying Luo CS 6520 Final Project Apr. 27, 2001 Problem Background Broadcasting in ad hoc mobile networks Flooding & Broadcast Storm Flooding: Each node retransmits the message t

• Bowling Green CS 4520
Dynamic Sets (III, Introduction) Dynamic sets (Data structures): we change a dictionary, add/remove words reuse of structured information on-line algorithms - very fast updating record x key sat-te data Elements: key field is the element ID,

• Bowling Green CS 4520
Binary Search Trees (13.1/12.1) Support Search, Minimum, Maximum, Predecessor, Successor, Insert, Delete In addition to key each node has left = left child, right = right child, p = parent Binary search tree property: all keys in the left subtr

• Bowling Green CS 4520
Master Method (4. 3) Recurrent formula T(n) = a T(n/b) + f(n) f (n) = O(n logb a ) for some > 0 then 1) if T (n) = (n logb a ) f (n) = (n logb a ) 2) if T (n) = ( n logb a log n) then ) 3) if f (n) = (n log b a + for some > 0 and a f(n/b) c f

• Bowling Green CS 4520
Graph Representation (23.1/22.1) HW: problem 23.3, p.496 G=(V, E) -graph: V = V(G) - vertices; E = E(G) - edges (connecting pairs of vertices) 1 Adjacency-list 1 2 3 4 5 3 3 5 5 2 4 2 3 4 5 Adjacency-matrix 1 1 2 3 4 5 0 0 0 0 0 2 0 0 0 0 1 3

• Bowling Green CS 4520
Midterm Midterm is Wednesday next week ! The quiz contains 5 problems = 50 min + 0 min more Master Theorem/ Examples Quicksort/ Mergesort Binary Heaps / Binary Search Trees Depth/Breadth First Search Greedy Algorithm / Prim\'s algorithm for MST

• Bowling Green CS 4520
All-Pairs Shortest Paths (26.0/25) HW: problem 26-1, p. 576/25-1, p. 641 Directed graph G = (V,E), weight E Goal: Create n n matrix of s-p distances (u,v) Running Bellman-Ford once from each vertex O( ) = O( ) on dense graphs Adjacency-matri