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Show Source |    | About   «  21.2. An Introduction to Problem Solving   ::   Contents   ::   21.4. Introduction to Analyzing a Problem  »

21.3. Semester Overview

21.3.1. Semester Overview

The central question for this course is: Given a problem, do we have (or can we devise) a good solution? Everything that we will do is connected to this central question in some way. Here "good" will usually be taken to mean "as efficient as the problem will allow".

If we have a problem and a proposed solution, then we need a mechanism that will let us assess whether that solution is good or not. This is where algorithm analysis comes in. This module is not a review of basic algorithm analysis. It is a reconstruction of the basic concept, from the ground up. Your prior study of algorithm analysis probably focussed on how to analyze a given program or algorithm. We will certainly do that during the course. But in this module, we will focus on the question of how algorithm analysis helps us to answer our central question.

This discussion assumes that you are roughly familiar with basic algorithm analysis terms and concepts. These include definitions for the terms problem versus algorithm versus program. These include the concepts of upper bounds, lower bounds, growth rate, best case, worst case, average case, big-oh notation, Omega notation, and Theta notation. Review any of this material as necessary before continuing.

Our problems must be well-defined enough to be solved on computers. (Actually, to solve a problem we need more than just a clear definition. By the end of the semester, we will discuss problems that are not computable (i.e., cannot be solved) even though their definition is clear.)

A problem is a function (i.e., a mapping of inputs to outputs). We have different problem instances (inputs) for the problem, where each instance has a size. To solve a problem, we must provide an algorithm, a coding of problem instances into inputs for the algorithm, and a coding for outputs into solutions.

An algorithm executes the mapping. A proposed algorithm must work for ALL instances (give the correct mapping to the output for that input instance). (Actually, we will relax this restriction later when we talk about Approximation and Probabilistic algorithms.)

Our goal is to solve problems with as little computational effort per instance as possible. We are most often interested in solutions to "large" instances of the problem (asymptotic Analysis). Occasionally we are concerned with small instances. Then, constants matter.

Ultimately, we want to solve a problem by using an efficient programs. But it is not a good idea to start by writing programs and then comparing them. We don't want to spend a lot of time writing worthless programs. We want a way to decide if the program is worth writing in the first place. So, we will really spend most of our time looking at algorithms instead of programs, and using algorithm analysis to evaluate the algorithms.

Algorithm analysis is essentially an exercise in modeling. A model is a simplification of reality that preserves only the essential elements. With a model, we can more easily focus on and reason about these essentials.

Our primary tactical concern for the semester will be how to recognize if an algorithm is efficient or not. We will need (and so will study) a lot of mathematical tools for this. Your primary tools will be summations and recurrence relations. Given the nature of many of our algorithms, we need to develop a lot of proficiency using logarithms.

21.3.1.1. Modeling Algorithm Cost

We want to measure the cost of an algorithm. We want this process to be as simple as possible. We need a yardstick to define the "cost" of the algorithm. Qualities for this yardstick are:

  • It should measure something that we care about. Usually we care about time, but not always.
  • It should by quantitative, allowing comparisons.
  • It should be easy to compute (the measure, not the algorithm).
  • It should be a good predictor of what a corresponding program would actually cost.

The fundamental driver for algorithm analysis is the behavior (growth rate) of a the algorithm as the problem size grows. Just to complicate things: Algorithms can behave very different on different inputs of a given size. The concepts of best, average, and worst cases come in here. To have a meaningful discussion about the behavior of an algorithm, we have to agree in advance about which of the many behaviors that algorithm might have in terms of its growth rate as the input size grows.

To model the growth rate of an algorithm, we need:

  • A measure for problem size.
  • A measure for solution effort.

To get a measurement, either for the problem size or the solution effort, we have to have a cost model. Here is a simple example. Assume that our problem is how to square a value. We will accept as our input size the value that we want to square. (Later on, we will actually come to realize that this is actually a lousy way to model input size for a numeric problem, but it will do for now.)

Here is an example of a model for a cost measure. Like any model, it might or might not be a good model. Look back at the list of qualities for a good model, and think about whether this example has those qualities or not as you read through it.

Our problem is to calculate the square of a number.

We will model the input size as simply the value of the number. (Later we will come to realize that this is a lousy model for the input size of a numeric problem. But its good enough for now.)

To model the cost of the solution, we will assume that asigning to a variable takes fixed time. We will also assume that all other operations take no time. (Is this a good model? Whether it is good or not, it is a model.)

Algorithm 1:

sum = n*n;

One assignment was made, so the cost is 1. Is this a good model for our intuitive notion of the cost for this code fragment? Most people would consider this a reasonable estimate of the work done for most purposes. So it looks like a reasonable model.

Algorithm 2:

sum = 0;
for (i=1; i<=n; i++)
  sum = sum + n;

The number of assignments made is

\[1 + \sum_{i=1}^{n} 1 = n + 1\]

Now, there is a lot of room for quibbling here. Depending on how you want to deal with loop variables, you might want to say that the number of assignments is \(2n + 1\). This makes a difference of \(n+1\) vs \(2n+1\). Does it matter? Not so much. We didn't know the exact amount of time for an operation to begin with, so the factor of 2 doesn't seem to mean much. What is important is that the growth rates of these two are the same. In fact, this is the key consideration. Perhaps we are concerned about whether an assignment is the same in real runtime cost as a multiplication, which might be different from an addition. Maybe incrementing a loop variable costs something different from doing an ordinary assignment. But really none of this matters when compared against the fundamental recognition that the cost of this algorithm is proportional to the input size (in this case, the value of the input variable). \(n+1\) and \(2n+1\) both have linear growth rate, so they are both equally predictive of the growth rate for the algorithm. If we all agree that this approch to squaring a number has a linear growth rate on the size of the number, then we can conclude that this is a reasonable model for the purpose of estimating the growth rate.

Algorithm 3:

sum = 0;
for (i=1; i<=n; i++)
  for (j=1; j<=n; j++)
    sum = sum + 1;

The number of assignments made is:

\[1 + \sum_{i=1}^{n} \sum_{j=1}^n 1 = n^2 + 1\]

Again, this is a reasonable model for the cost of this algorithm.

Now, given three algorithms, and with a model in hand for measuring their costs, the next question is: Which of these three algorithms is best (meaning, requires the least amount of work to run)? Obviously we consider the first to be best in this sense.

In comparison to the above example, consider a problem that involves string assignment (done by copying the characters in the string). In this case, is our model that assignment has contant time cost still good? Think about this.

As another example of modeling: Consider a problem that works on a list, and an important basic operation is accessing the \(i\mathrm{th}\) record on the list. We can take as our model that such an access requires one unit of work. If the list is implemented using an array in memory, then we probably consider this to be a "reasonable" model. If the list is implemented using a singly linked list, then we probably do not consider this to be a "reasonable" model. (Why?)

21.3.1.2. Big Issues

How do we create an efficient algorithm? We use problem solving and algorithm design skills. This semester, we will see some standard algorithm design techniques. One good example of such a design technique that works for a lot of problems is dynamic programming.

How do we recognize a "good" algorithm? This is a key issue, because we don't know whether to stop with trying to create a "good" algorithm unless we can recognize one. Our answer is: By the relationship of its performance to the intrinsic difficulty of the problem. Of course, that requires a measure for the algorithm's performance and a measure for the intrinsic difficulty of the problem.

How "hard" is a problem? That is, what is its intrinsic difficulty? This is where the concept of the lower bound for a problem comes in. For now, we will restrict the term "hard" to mean "How much does it cost to run?" Later, we will talk about some different meanings for the term "hard".

As we go through a series of problems this semester, we will use the following general plan:

  • Define a PROBLEM.
  • Build a MODEL to measure the size of the input, and the cost of a solution to the problem.
  • Design an ALGORITHM to solve the problem.
  • ANALYZE both problem and algorithm under the model.
    • Analyze an algorithm to get an UPPER BOUND.
    • Analyze a problem to get a LOWER BOUND.
  • COMPARE the bounds to see if our solution is "good enough".

If the two bounds that we compute do not match, then here are some options:

  • Redesign the algorithm, or invent a new one.
  • Tighten the bounds (if they were not already tight).
  • Change the model.
  • Change the problem.

   «  21.2. An Introduction to Problem Solving   ::   Contents   ::   21.4. Introduction to Analyzing a Problem  »

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