Download
1 / 49
490 likes | 646 Views
Week 12 - Monday. CS322. Last time. What did we talk about last time? Trees Graphing functions. Questions?. Logical warmup. Two vegetarians and two cannibals are on one bank of a river They have a boat that can hold at most two people
E N D
Week 12 - Monday CS322
Last time • What did we talk about last time? • Trees • Graphing functions
Logical warmup • Two vegetarians and two cannibals are on one bank of a river • They have a boat that can hold at most two people • Come up with a sequence of boat loads that will convey all four people safely to the other side of the river • The cannibals on any given bank cannot outnumber the vegetarians…. or else!
Spanning Trees Which we somehow seemed to skip earlier…
Turning graphs into trees • Consider the following graph that shows all the routes an airline has between cities Detroit Minneapolis Milwaukee Chicago Cincinnati Louisville St. Louis Nashville
Turning graphs into trees • What if we want to remove routes (to save money)? • How can we keep all cities connected? Detroit Minneapolis Milwaukee Chicago Cincinnati Louisville St. Louis Nashville
The best tree? • Does this tree have the smallest number of routes? • Why? Detroit Minneapolis Milwaukee Chicago Cincinnati Louisville St. Louis Nashville
Spanning trees • A spanning tree for a graph G is a subgraph of G that contains every vertex of G and is a tree • Some properties: • Every connected graph has a spanning tree • Why? • Any two spanning trees for a graph have the same number of edges • Why?
Weighted graphs • In computer science, we often talk about weighted graphs when tackling practical applications • A weighted graph is a graph for which each edge has a real number weight • The sum of the weights of all the edges is the total weight of the graph • Notation: If e is an edge in graph G, then w(e) is the weight of e and w(G) is the total weight of G • A minimum spanning tree (MST) is a spanning tree of lowest possible total weight
Weighted graphs example • Here is the graph from before, with labeled weights Detroit Minneapolis 355 Milwaukee 74 230 306 695 348 Chicago Cincinnati 269 262 83 242 Louisville St. Louis 151 Nashville
Finding a minimum spanning tree • Kruskal's algorithm gives an easy to follow technique for finding an MST on a weighted, connected graph • Informally, go through the edges, adding the smallest one, unless it forms a circuit • Algorithm: • Input: Graph G with n vertices • Create a subgraphT with all the vertices of G (but no edges) • Let E be the set of all edges in G • Set m = 0 • While m < n – 1 • Find an edge e in E of least weight • Delete e from E • If adding e to T doesn't make a circuit • Add e to T • Set m = m + 1 • Output: T
Minimum spanning tree example • Run Kruskal's algorithm on the city graph: Detroit Minneapolis 355 Milwaukee 74 230 306 695 348 Chicago Cincinnati 269 262 83 242 Louisville St. Louis 151 Nashville
Minimum spanning tree output • Output: Detroit Minneapolis 355 Milwaukee 74 230 Chicago Cincinnati 262 83 242 Louisville St. Louis 151 Nashville
Prim's algorithm • Prim's algorithm gives another way to find an MST • Informally, start at a vertex and add the next closest node not already in the MST • Algorithm: • Input: Graph G with n vertices • Let subgraphT contain a single vertex v from G • Let V be the set of all vertices in G except for v • For i from 1 to n – 1 • Find an edge e in G such that: • e connects T to one of the vertices in V • e has the lowest weight of all such edges • Let w be the endpoint of e in V • Add e and w to T • Delete w from V • Output: T
Prim fights Kruskal • Apply Kruskal's algorithm to the graph below • Now, apply Prim's algorithm to the graph below • Is there any other MST we could make? 3 2 1 4 1 3 1
Discontinuous functions • Recall the definition of the floor of x: • x = the largest integer that is less than or equal to x • Graph f(x) = x • Defining functions on integers instead of real values affects their graphs a great deal • Graph p1(x) = x, x R • Graph f(n) = n, n N
Multiples of functions • There is a strong visual (and of course mathematical) correlation a function that is the multiple of another function • Examples: • g(x) = x + 2 • 2g(x) = 2x + 4 • Given f graphed below, sketch 2f 2 1 -6 -5 -4 -3 -2 -1 -1 -2 1 2 3 4 5 6
Absolute value • Consider the absolute value function • f(x) = |x| • Left of the origin it is constantly decreasing • Right of the origin it is constantly increasing 2 1 -6 -5 -4 -3 -2 -1 -1 -2 1 2 3 4 5 6
Increasing and decreasing functions • We say that f is decreasing on the set Siff for all real numbers x1 and x2 in S, if x1 < x2, then f(x1) > f(x2) • We say that f is increasing on the set Siff for all real numbers x1 and x2 in S, if x1 < x2, then f(x1) < f(x2) • We say that f is an increasing (or decreasing) function ifff is increasing (or decreasing) on its entire domain • Clearly, a positive multiple of an increasing function is increasing • Virtually all running time functions are increasing functions
Asymptotic Notation (Big Oh) Student Lecture
Growth of functions • Mathematicians worry about the growth of various functions • They usually express such things in terms of limits, maybe with derivatives • We are focused primarily on functions that bound running time spent and memory consumed • We just need a rough guide • We want to know the order of the growth
Definitions • Let f and g be real-valued functions defined on the same set of nonnegative real numbers • f is of order at least g, written f(x) is(g(x)), iff there is a positive A R and a nonnegative a R such that • A|g(x)| ≤ |f(x)| for all x > a • f is of order at most g, written f(x) isO(g(x)), iff there is a positive B R and a nonnegative b R such that • |f(x)| ≤ B|g(x)| for all x > b • f is of order g, written f(x) is(g(x)), iff there are positive A, B R and a nonnegative k R such that • A|g(x)| ≤ |f(x)| ≤ B|g(x)| for all x > k
Using the notation • Express the following statements using appropriate notation: • 10|x6| ≤ |17x6 – 45x3 + 2x + 8| ≤ 30|x6|, for x > 2 • Justify the following: • is (x)
Properties of -, O-, and - notation • Let f and g be real-valued functions defined on the same set of nonnegative real numbers • f(x) is (g(x)) and f(x) is O(g(x)) ifff(x) is (g(x)) • f(x) is (g(x)) iffg(x) is O(f(x)) • f(x) is (f(x)), f(x) is O(f(x)), and f(x) is (f(x)) • If f(x) is O(g(x)) and g(x) is O(h(x)) then f(x) is O(h(x)) • If f(x) is O(g(x)) and c is a positive real, then cf(x) is O(g(x)) • If f(x) is O(h(x)) and g(x) is O(k(x)) then f(x) + g(x) is O(G(x)) where G(x) = max(|h(x)|,|k(x)|) for all x • If f(x) is O(h(x)) and g(x) is O(k(x)) then f(x)g(x) is O(h(x)k(x))
Orders of functions • If 1 < x, then • x < x2 • x2 < x3 • … • So, for r, sR, where r < s and x > 1, • xr < xs • By extension, xr is O(xs)
Proving bounds • Prove a bound for g(x) = (1/4)(x – 1)(x + 1) for x R • Prove that x2 is not O(x) • Hint: Proof by contradiction
Polynomials • Let f(x) be a polynomial with degree n • f(x) = anxn + an-1xn-1 + an-2xn-2 … + a1x + a0 • By extension from the previous results, if an is a positive real, then • f(x) is O(xs) for all integers s n • f(x) is (xr) for all integers r≤n • f(x) is (xn) • Furthermore, let g(x) be a polynomial with degree m • g(x) = bmxm + bm-1xm-1 + bm-2xm-2 … + b1x + b0 • If an and bm are positive reals, then • f(x)/g(x) is O(xc) for real numbers c> n - m • f(x)/g(x) is not O(xc) for all integers c > n -m • f(x)/g(x) is (xn- m)
Extending notation to algorithms • We can easily extend our -, O-, and - notations to analyzing the running time of algorithms • Imagine that an algorithm A is composed of some number of elementary operations (usually arithmetic, storing variables, etc.) • We can imagine that the running time is tied purely to the number of operations • This is, of course, a lie • Not all operations take the same amount of time • Even the same operation takes different amounts of time depending on caching, disk access, etc.
Running time • First, assume that the number of operations performed by A on input size n is dependent only on n, not the values of the data • If f(n) is (g(n)), we say that Ais(g(n)) or that A is of order g(n) • If the number of operations depends not only on n but also on the values of the data • Let b(n) be the minimum number of operations where b(n) is (g(n)), then we say that in the best case, Ais(g(n)) or that A has a best case order of g(n) • Let w(n) be the maximum number of operations where w(n) is (g(n)), then we say that in the worst case, Ais(g(n)) or that A has a worst case order of g(n)
Computing running time • With a single for (or other) loop, we simply count the number of operations that must be performed: int p = 0; int x = 2; for( inti = 2; i <= n; i++ ) p = (p + i)*x; • Counting multiplies and adds, (n – 1) iterations times 2 operations = 2n – 2 • As a polynomial, 2n – 2 is (n)
Nested loops • When loops do not depend on each other, we can simply multiply their iterations (and asymptotic bounds) int p = 0; for( inti = 2; i <= n; i++ ) for( int j = 3; j <= n; j++ ) p++; • Clearly (n – 1)(n -2) is (n2)
Trickier nested loops • When loops depend on each other, we have to do more analysis int s = 0; for( inti = 1; i <= n; i++ ) for( int j = 1; j <= i; j++ ) s = s + j*(i – j + 1); • What's the running time here? • Arithmetic sequence saves the day (for the millionth time)
Iterations with floor • When loops depend on floor, what happens to the running time? int a = 0; for( inti = n/2; i <= n; i++ ) a = n - i; • Floor is used implicitly here, because we are using integer division • What's the running time? Hint: Consider n as odd or as even separately
Sequential search • Consider a basic sequential search algorithm: int search( int[]array, int n, int value) { for( inti = 0; i < n; i++ ) if( array[i] == value ) returni; return -1; } • What's its best case running time? • What's its worst case running time? • What's its average case running time?
Insertion sort algorithm • Insertion sort is a common introductory sort • It is suboptimal, but it is one of the fastest ways to sort a small list (10 elements or fewer) • The idea is to sort initial segments of an array, insert new elements in the right place as they are found • So, for each new item, keep moving it up until the element above it is too small (or we hit the top)
Insertion sort in code public static void sort( int[]array, int n) { for( inti = 1; i < n; i++ ) { intnext = array[i]; int j = i - 1; while( j != 0 && array[j] > next ) { array[j+1] = array[j]; j--; } array[j] = next; } }
Best case analysis of insertion sort • What is the best case analysis of insertion sort? • Hint: Imagine the array is already sorted
Worst case analysis of insertion sort • What is the worst case analysis of insertion sort? • Hint: Imagine the array is sorted in reverse order
Average case analysis of insertion sort • What is the average case analysis of insertion sort? • Much harder than the previous two! • Let's look at it recursively • Let Ek be the average number of comparisons needed to sort k elements • Ek can be computed as the sum of the average number of comparisons needed to sort k – 1 elements plus the average number of comparisons (x) needed to insert the kth element in the right place • Ek = Ek-1 + x
Finding x • We can employ the idea of expected value from probability • There are k possible locations for the element to go • We assume that any of these k locations is equally likely • For each turn of the loop, there are 2 comparisons to do • There could be 1, 2, 3, … up to k turns of the loop • Thus, weighting each possible number of iterations evenly gives us
Finishing the analysis • Having found x, our recurrence relation is: • Ek = Ek-1 + k + 1 • Sorting one element takes no time, so E1 = 0 • Solve this recurrence relation! • Well, if you really banged away at it, you might find: • En = (1/2)(n2 + 3n – 4) • By the polynomial rules, this is (n2) and so the average case running time is the same as the worst case
Next time… • Finish algorithmic efficiency • Exponential and logarithmic functions
Reminders • Keep reading Chapter 11 • Keep working on Assignment 9 • Due Friday before midnight • Exam 3 is next Monday • Review on Friday
