CSE 830: Design and Theory of Algorithms Dr. Eric Torng TA: Carl Bussema Many slides adapted from those used by Dr. Charles Ofria Welcome to CSE 830, the Design and Theory of Algorithms. My name is Charles, and I will be your instructor for this class. The goal of the class is to teach you the fundamentals of creating and analyzing computer algorithms. Before we get to the actual material, however, I need to go through a few administrative details. While I take attendance, I have some handouts for you to be reading over.

Outline Definitions Course Objectives Administrative stuff … Algorithms Problems Course Objectives Administrative stuff … Analysis of Algorithms

What is an Algorithm? Algorithms are the ideas behind computer programs. An algorithm is the thing that stays the same whether the program is in C++ running on a Cray in New York or is in BASIC running on a Macintosh in Alaska! To be interesting, an algorithm has to solve a general, specified problem.

What is a problem? Definition Problem Specification Example: Sorting A mapping/relation between a set of input instances (domain) and an output set (range) Problem Specification Specify what a typical input instance is Specify what the output should be in terms of the input instance Example: Sorting Input: A sequence of N numbers a1…an Output: the permutation (reordering) of the input sequence such that a1  a2  …  an .

Types of Problems Search: find X in the input satisfying property Y Structuring: Transform input X to satisfy property Y Construction: Build X satisfying Y Optimization: Find the best X satisfying property Y Decision: Does X satisfy Y? Adaptive: Maintain property Y over time.

Two desired properties of algorithms Correctness Always provides correct output when presented with legal input Efficiency Computes correct output quickly given input

Correctness Example: Traveling Salesperson Problem (TSP) Input: A sequence of N cities with the distances dij between each pair of cities Output: a permutation (ordering) of the cities <c1’, …, cn’> that minimizes the expression Σj =1 to n-1 dj’,j’+1 + dn’,1’ Which of the following algorithms is correct? Nearest neighbor: Initialize tour to city 1. Extend tour by visiting nearest unvisited city. Finally return to city 1. All tours: Try all possible orderings of the points selecting the ordering that minimizes the total length:

Efficiency Example: Odd Number Problem Input: A number n Output: Yes if n is odd, no if n is even Which of the following algorithms is most efficient? Count up to that number from one and alternate naming each number as odd or even. Factor the number and see if there are any twos in the factorization. Keep a lookup table of all numbers from 0 to the maximum integer. Look at the last bit (or digit) of the number.

Outline Definitions Course Objectives Administrative stuff … Algorithms Problems Course Objectives Administrative stuff … Analysis of Algorithms

Course Objectives Details of classic algorithms Methods for designing algorithms Validate/verify algorithm correctness Analyze algorithm efficiency Prove (or at least indicate) no correct, efficient algorithm exists for solving a given problem Writing clear algorithms and proofs

Classic Algorithms Lots of wonderful algorithms have already been developed I expect you to learn most of this from reading, though we will reinforce in lecture

Algorithm design methods Something of an art form Cannot be fully automated We will describe some general techniques and try to illustrate when each is appropriate

Algorithm correctness Proving an algorithm generates correct output for all inputs One technique covered in textbook Loop invariants We will do some of this in the course, but it is not emphasized as much as other objectives

Analyzing algorithms The “process” of determining how much resources (time, space) are used by a given algorithm We want to be able to make quantitative assessments about the value (goodness) of one algorithm compared to another We want to do this WITHOUT implementing and running an executable version of an algorithm

Proving hardness results We believe that no correct and efficient algorithm exists that solves many problems such as TSP We define a formal notion of a problem being hard We develop techniques for proving hardness results

Clear Writing Methods for Expressing Algorithms Implementations Pseudo-code English Writing clear and understandable proofs My main concern is not the specific language used but the clarity of your algorithm/proof In this class, most algorithms will be expressed in plain English with pseudo-code used to clear up any ambiguities.

Outline Definitions Course Objectives Administrative stuff … Algorithms Problems Course Objectives Administrative stuff … Analysis of Algorithms

Algorithm Analysis Overview RAM model of computation Concept of input size Measuring complexity Best-case, average-case, worst-case Asymptotic analysis Asymptotic notation

The RAM Model RAM model represents a “generic” implementation of the algorithm Each “simple” operation (+, -, =, if, call) takes exactly 1 step. Loops and subroutine calls are not simple operations, but depend upon the size of the data and the contents of a subroutine. We do not want “sort” to be a single step operation. Each memory access takes exactly 1 step. Basically, any basic operation that takes a small, fixed amount of time we assume to take just one step. We measure the run time of an algorithm by counting the number of steps it takes. Why does this work? For the same reason that the “Flat Earth” model works. In our day-to-day lives we assume the Earth to be flat! Now, lets look at how we can use this.

Input Size In general, larger input instances require more resources to process correctly We standardize by defining a notion of size for an input instance Examples What is the size of a sorting input instance? What is the size of an “Odd number” input instance?

Algorithm Analysis Overview RAM model of computation Concept of input size Measuring complexity Best-case, average-case, worst-case Asymptotic analysis Asymptotic notation

Measuring Complexity The running time of an algorithm is the function defined by the number of steps (or amount of memory) required to solve input instances of size n F(1) = 3 F(2) = 5 F(3) = 7 … F(n) = 2n+1 Problem: Inputs of the same size may require different numbers of steps to solve

3 different analyses The worst case running time of an algorithm is the function defined by the maximum number of steps taken on any instance of size n. The best case running time of an algorithm is the function defined by the minimum number of steps taken on any instance of size n. The average-case running time of an algorithm is the function defined by an average number of steps taken on any instance of size n. Which of these is the best to use?

Average case analysis Drawbacks Based on a probability distribution of input instances The distribution may not be appropriate Provides little consolation if we have a worst-case input More complicated to compute than worst case running time Worst case running time is often comparable to average case running time (see next graph) Counterexamples to above point: Quicksort simplex method for linear programming

Best, Worst, and Average Case

Worst case analysis Typically much simpler to compute as we do not need to “average” performance on many inputs Instead, we need to find and understand an input that causes worst case performance Provides guarantee that is independent of any assumptions about the input Often reasonably close to average case running time The standard analysis performed

Algorithm Analysis Overview RAM model of computation Concept of input size Measuring complexity Best-case, average-case, worst-case Asymptotic analysis Asymptotic notation

Motivation for Asymptotic Analysis An exact computation of worst-case running time can be difficult Function may have many terms: 4n2 - 3n log n + 17.5 n - 43 n⅔ + 75 An exact computation of worst-case running time is unnecessary Remember that we are already approximating running time by using RAM model

Simplifications Ignore constants Asymptotic Efficiency 4n2 - 3n log n + 17.5 n - 43 n⅔ + 75 becomes n2 – n log n + n - n⅔ + 1 Asymptotic Efficiency n2 – n log n + n - n⅔ + 1 becomes n2 End Result: Θ(n2)

Why ignore constants? RAM model introduces errors in constants Do all instructions take equal time? Specific implementation (hardware, code optimizations) can speed up an algorithm by constant factors We want to understand how effective an algorithm is independent of these factors Simplification of analysis Much easier to analyze if we focus only on n2 rather than worrying about 3.7 n2 or 3.9 n2

Asymptotic Analysis We focus on the infinite set of large n ignoring small values of n Usually, an algorithm that is asymptotically more efficient will be the best choice for all but very small inputs. 

“Big Oh” Notation O(g(n)) = {f(n) : there exist positive constants c and n0 such that " n≥n0, 0 ≤ f(n) ≤ c g(n) } What are the roles of the two constants? n0: c:  n0 f(n) ≤ c g(n)

Set Notation Comment O(g(n)) is a set of functions. However, we will use one-way equalities like n = O(n2) This really means that function n belongs to the set of functions O(n2) Incorrect notation: O(n2) = n Analogy “A dog is an animal” but not “an animal is a dog”

Three Common Sets f(n) = O(g(n)) means c  g(n) is an Upper Bound on f(n) f(n) = (g(n)) means c  g(n) is a Lower Bound on f(n) f(n) = (g(n)) means c1  g(n) is an Upper Bound on f(n) and c2  g(n) is a Lower Bound on f(n) These bounds hold for all inputs beyond some threshold n0. Asymptotic or Big-O notation. O Omega Sigma

O(g(n))

(g(n))

(g(n))

O(f(n)) and (g(n))

Example Function f(n) = 3n2 - 100n + 6

Quick Questions c n0 3n2 - 100n + 6 = O(n2) 3n2 - 100n + 6 = O(n3) [ Do on blackboard!!!!! ] f(n) = 3n2 - 100n + 6

Common Complexity Functions n 110-5 sec 210-5 sec 310-5 sec 410-5 sec 510-5 sec 610-5 sec n2 0.0001 sec 0.0004 sec 0.0009 sec 0.016 sec 0.025 sec 0.036 sec n3 0.001 sec 0.008 sec 0.027 sec 0.064 sec 0.125 sec 0.216 sec n5 0.1 sec 3.2 sec 24.3 sec 1.7 min 5.2 min 13.0 min 2n 0.001sec 1.0 sec 17.9 min 12.7 days 35.7 years 366 cent 3n 0.59sec 58 min 6.5 years 3855 cent 2108cent 1.31013cent log2 n 310-6 sec 410-6 sec 510-6 sec 510-6 sec 610-6 sec 610-6 sec n log2 n 310-5 sec 910-5 sec 0.0001 sec 0.0002 sec 0.0003 sec 0.0004 sec

Example Problems 1. What does it mean if: f(n)  O(g(n)) and g(n)  O(f(n)) ? 2. Is 2n+1 = O(2n) ? Is 22n = O(2n) ? 3. Does f(n) = O(f(n)) ? 4. If f(n) = O(g(n)) and g(n) = O(h(n)), can we say f(n) = O(h(n)) ?

Extra Slides Slides illustrating TSP algorithms Case study with Insertion sort for Best, Average, Worst case analysis

Possible Algorithm: Nearest neighbor

Not Correct!

A Correct Algorithm Try all possible orderings of the points selecting the ordering that minimizes the total length: d =  For each of the n! permutations, Pi of the n points, if cost(Pi) < d then d = cost(Pi) Pmin = Pi return Pmin

Case study: Insertion Sort Count the number of times each line will be executed: Num Exec. for i = 2 to n key = A[i] j = i - 1 while j > 0 AND A[j] > key A[j+1] = A[j] j = j -1 A[j+1] = key n-1 (or n) for all but while loop varies for while loop