Probabilistic (Average-Case) Analysis and Randomized Algorithms Two different but similar analyses –Probabilistic analysis of a deterministic algorithm –Analysis of a Randomized algorithm Tools from probability theory –Indicator variables –Linearity of expectation Example Problems/Algorithms –Hiring problem (Chapter 5) –Biased coin –Sorting/Quicksort (Chapter 7) –Hat Check Problem –Online Hiring Problem

Probabilistic (Average-Case) Analysis Algorithm is deterministic; for a fixed input, it will run the same every time Analysis Technique –Assume a probability distribution for your inputs –Analyze item of interest over the distribution Caveats –Specific inputs may have much worse performance –If the distribution is wrong, analysis may give misleading picture

Randomized Algorithm “Randomize” the algorithm; for a fixed input, it will run differently depending on the result of random “coin tosses” Randomization examples/techniques –Randomize the order that candidates arrive –Randomly select a pivot element –Randomly select from a collection of deterministic algorithms Key points –Works well with high probability on every input –May fail on every input with low probability

Key Analysis Tools Indicator variables –Suppose we want to study random variable X that represents a composite of many random events –Define a collection of “indicator” variables X i that focus on individual events; typically X =  X i Linearity of expectations –Let X, Y, and Z be random variables s.t. X = Y + Z –Then E[X] = E[Y+Z] = E[Y] + E[Z] Recurrence Relations

Hiring Problem Input –A sequence of n candidates for a position –Each has a distinct quality rating that we can determine in an interview Algorithm –Current = 0; –For k = 1 to n If candidate(k) is better than Current, hire(k) and Current = k; Cost: –Number of hires Worst-case cost is n

Analyze Hiring Problem Assume a probability distribution –Each of the n! permutations is equally likely Analyze item of interest over probability distribution –Define random variables Let X = random variable corresponding to # of hires Let X i = “indicator variable” that i th interviewed candidate is hired –Value 0 if not hired, 1 if hired X =  i = 1 to n X i –E[X i ] = ? Explain why: –E[X] = E[  i = 1 to n X i ] =  i = 1 to n E[X i ] = ? Key observation: linearity of expectations

Alternative analysis Analyze item of interest over probability distribution –Let X i = indicator random variable that the i th best candidate is hired 0 if not hired, 1 if hired –Questions Relationship of X to X i ? E[X i ] = ?  i = 1 to n E[X i ] = ?

Questions What is the probability you will hire n times? What is the probability you will hire exactly twice? Biased Coin –Suppose you want to output 0 with probability ½ and 1 with probability ½ –You have a coin that outputs 1 with probability p and 0 with probability 1-p for some unknown 0 < p < 1 –Can you use this coin to output 0 and 1 fairly? –What is the expected running time to produce the fair output as a function of p?

Quicksort Algorithm Overview –Choose a pivot element for example the last element –Partition elements to be sorted based on partition element –Recursively sort smaller and larger elements

Quicksort Walkthrough

Formal Average-case Analysis Let X denote the random variable that represents the total number of comparisons performed Let indicator variable X ij = the event that the ith smallest element and jth smallest element are compared –0 if not compared, 1 if compared X =  i=1 to n-1  j=i+1 to n X ij E[X] =  i=1 to n-1  j=i+1 to n E[X ij ]

Computing E[X ij ] E[X ij ] = probability that i and j are compared Observation –All comparisons are between a pivot element and another element –If an item k is chosen as pivot where i < k < j, then items i and j will not be compared E[X ij ] = –Items i or j must be chosen as a pivot before any items in interval (i..j)

Computing E[X] E[X] = Σ i=1 to n-1 Σ j=i+1 to n E[X ij ] = Σ i=1 to n-1 Σ j=i+1 to n 2/(j-i+1) = Σ i=1 to n-1 Σ k=1 to n-i 2/(k+1) ≤ Σ i=1 to n-1 2 H n-i+1 ≤ Σ i=1 to n-1 2 H n = 2 (n-1)H n

Alternative Average-case Analysis Let T(n) denote the average time required to sort an n-element array –“Average” assuming each permutation equally likely Write a recurrence relation for T(n) –T(n) = Using induction, we can then prove that T(n) = O(n log n) –See homework 2, problem 6 for more details

Avoiding the worst-case Understanding quicksort’s worst-case Methods for avoiding it –Pivot strategies –Randomization

Understanding the worst case A B D F H J K A B D F H J A B D F H A B D F A B D A B A The worst case occur is a likely case for many applications.

Pivot Strategies Use the middle Element of the sub-array as the pivot. Use the median element of (first, middle, last) to make sure to avoid any kind of pre- sorting. What is the worst-case performance for these pivot selection mechanisms?

Randomized Quicksort Make chance of worst-case run time equally small for all inputs Methods –Choose pivot element randomly from range [low..high] –Initially permute the array

Hat Check Problem N people give their hats to a hat-check person at a restaurant The hat-check person returns the hats to the people randomly What is the expected number of people who get their own hat back?

Online Hiring Problem Input –A sequence of n candidates for a position –Each has a distinct quality rating that we can determine in an interview We know total ranking of interviewed candidates, but not with respect to candidates left to interview –We can hire only once Online Algorithm Hire(k,n) –Current = 0; –For j = 1 to k If candidate(j) is better than Current, Current = j; –For j = k+1 to n If candidate(j) is better than Current, hire(j) and return Questions: –What is probability we hire the best qualified candidate given k? –What is best value of k to maximize above probability?

Online Hiring Analysis Let S be the probability that we successfully hire the best qualified candidate Let S i be the probability that we successfully hire the best qualified candidate AND this candidate was the ith one interviewed What needs to happen for S i to be true? –Best candidate is in position i: B i –No candidate in positions k+1 through i-1 are hired: O i –These two quantities are independent, so we can multiply their probabilities to get S i

Computing S B i = 1/n O i = k/(i-1) S i = k/(n(i-1)) S = Σ i>k S i = k/n Σ i>k 1/(i-1) is probability of success k/n (H n – H k ): roughly k/n (ln n – ln k) Maximized when k = n/e Leads to probability of success of 1/e