# Math 373 Fall 2003

## General Information

There is only one section (01) of 373 this semester. It is taught by Prof. Bumby and meets MW5 (2:50 - 4:10 PM) in ARC-107.  Information about that section appears on this page.  For general information about the course, see the Math 373 page.

The final exam is scheduled for Monday, December 22, 8-11 AM in ARC 107.

## Textbook

Richard L. Burden & J. Douglas Faires; Numerical Analysis (seventh edition);Brooks/Cole, 1997 (841 pp.); (ISBN# 0-534-38216-9)

The course will cover (almost all of) Chapters 1 through 5, as described below.

• Chapter 1 Mathematical Preliminaries. Treatment will be informal (i.e., not considered as a lecture topic) and asynchronous (i.e., introduced as needed). It would be a good idea to read through this chapter early and often.
• Chapter 2 Solution of Equations in One Variable. This is something you do every day! The equations that can be solved numerically are much more general than those having exact algebraic solutions. For example, you can put your calculator in radian mode and repeatedly press the COS key. After about 30 steps, the display won't change when you press the key, so you have found a number whose cosine is itself. We will determine why this works. In this case, we started with the process and interpreted the result. To be useful, however, we need to start with an equation we want to solve and develop a process that will give the desired answer. This can be done by the famous Newton's Method. We will see why this method is so famous.
• Chapter 3 Interpolation and Polynomial Approximation. A polynomial of degree 1 is determined by its values at any 2 different points; A polynomial of degree 2 is determined by its values at any 3 different points; etc. Several interpretations of this will be given.
• Chapter 4 Numerical Differentiation and Integration. Calculus gives you a mechanical way to compute derivatives of functions given by a formula, but finds integration to be difficult. If you have only numerical information about a function, the reverse is true: integration is completely straightforward, but accuracy is lost in differentiation. The loss of accuracy in differentiation is the perfect context in which to study the different types of errors in numerical work, so topics like the acceleration of convergence appear in this chapter. The simpler formulas for numerical integration, including Simpson's Rule, which you probably met in calculus (and is likely to be what your calculator does when you ask it to integrate), can be analyzed using interpolation results from Chapter 3. This will also reveal limitations in these methods and suggest ways to overcome them.
• Chapter 5 Initial-Value Problems for Ordinary Differential Equations. You should have met Euler's method in CALC4. The ease of analyzing the method will allow us to show that it isn't very useful. However, a similar error analysis can be made for more complicated methods. By covering Euler's method in detail, we can skip the technical parts of the analysis of higher order methods, without making the theorems any less believable.

## Terminology

This list starts with one built for this course in Fall 2000 and may grow during this semester.
A process for numerical integration or solution of differential equations in which the step between computed values changes to keep the anticipated error at an acceptable level. The step can be larger (to limit ROUNDOFF) when the TRUNCATION error is small, but small when needed to control the TRUNCATION error.
AGM
The Arithmetic-Geometric Mean. The limit of an iterative process in which a pair of positive real numbers is successively related to the pair consisting of their arithmetic mean and their geometric mean. The process exhibits QUADRATIC CONVERGENCE, but it is interesting to relate the limit to other quantities.
BISECTION
A ROOTFINDING method that starts with an interval on which a function changes sign and repeatedly cuts the interval in half and selects the half on which the function changes sign. After n steps the size of the interval has been multiplied by 2-n. Since this estimate on the root is guaranteed, this rate of convergence is the minimal acceptable performance of a numerical method. It corresponds to LINEAR CONVERGENCE for an ITERATIVE method. This process gives a proof of the intermediate value theorem. The assumption of continuity on the theoretical side simply asserts that the function doesn't change much on a small enough interval. This is an abstract way of expressing that the function can be computed.
BLUNDER
If you say that six times nine is forty-two, that is not an error (as we use the word in this course), it is a blunder. The analysis of methods only works if every calculation does what it claims to do within known limitations. Hand calculations should be checked and programs tested with data for which the results can be predicted before answers can be trusted. (See also ERROR)
COMPOSITE INTEGRATION RULE
A formula for NUMERICAL INTEGRATION formed by dividing the domain of the integral into a large number of pieces, generally of equal length, and applying a simple rule, e.g., the MIDPOINT RULE, TRAPEZOIDAL RULE, or SIMPSON'S RULE on each piece. It is customary to combine the contribution of division points as endpoints of two subintervals, but this should be considered as post-processing since it destroys the structure of the expression used to approximate the integral.
DECIMAL PLACES
When we say that pi is 3.1416 to four decimal places, we mean that we have identified the closest rational number with denominator 10000 to pi. When more places are computed, the 6 at the end may (and, in fact, does) change to 5 followed by a large digit (e.g., 9 in this case). More loosely, when we describe a computation as being accurate to n decimal places, we mean that the true result lies in an interval of diameter 10-n centered at the computed value (or equivalently the computed value lies in an interval of diameter 10-n centered at the true result). To emphasize the level of accuracy, all n digits are written, even if the last digit is a zero. (see also SIGNIFICANT FIGURES)
DIVIDED DIFFERENCES
A collection of quantities that, at level zero, are the values of a function at a list of points, and are constructed at higher levels by a recursive construction. A difference at level n+1 is based on n+2 function values, and formed by subtracting two terms at level n that have n points in common and dividing by the difference of the independent variable for the other point in each of the terms. In particular, at level one, the result is just the difference quotient of the function at two points. Many expressions for the ERROR due to TRUNCATION can be expressed directly in terms of divided differences. This expression is converted using a generalized MEAN VALUE THEOREM into an expression involving a derivative of a suitable order.
ERROR
This is the bread and butter of Numerical Analysis. Every time an exact formula is replaced by an approximate one, or a real number replaced by something that can be represented on your computational device, there is an error. The subject seeks to find provable upper bounds for such differences and to find methods for which these bounds are small. (see also BLUNDER)
EULER-MACLAURIN SUMMATION
A formula in which the difference between an integral and its trapezoidal approximation is expanded as an asymptotic series. This can be used to provide the error terms needed to justify the ROMBERG INTEGRATION method.
EULER'S METHOD
A simple numerical method for solving differential equations. It is not practical, but it is easy to analyze and can be used to prove a version of the existence/uniqueness theorem for initial value problems.
EXTRAPOLATION
This could mean the use of an INTERPOLATION formula outside the interval where values of the function are known, but we have seen that this is not very reliable as a numerical method. Instead, this term is used to describe a technique to predict the limit of a sequence of calculations. It is similar to STEFFENSEN ACCELERATION, but its principle use is the construction of higher order formulas from simpler ones. We met it first in connection with NUMERICAL DIFFERENTIATION.
HERMITE INTERPOLATION
A representation of a function that combines features of TAYLOR SERIES with the LAGRANGE or NEWTON INTERPOLATION formulas. For example, if values of a function and its derivative are known at a set of points, a DIVIDED DIFFERENCE table can be constructed with each point appearing twice. The derivative at a point is used for the divided difference of a repeated point which would otherwise require division by zero. Once these values are inserted, the rest of the divided difference table is constructed in the usual way and the quantities at the top of each column are the coefficients of the terms as in the NEWTON INTERPOLATION FORMULA.
INITIAL VALUE PROBLEMS
A problem of finding functions of an independent variable given expressions for their derivatives in terms of "dependent variables" representing the functions and the independent variable together with values of all of the dependent variables at one particular value of the independent variable.
INTERPOLATION
A process for using values of a function at a few points to derive a formula for computing the function at an arbitrary point of an interval. Typically, this uses the given data to find a polynomial taking the same values, since polynomials are easily evaluated at any given point. Important expressions for this polynomial are the LAGRANGE INTERPOLATION FORMULA and the NEWTON INTERPOLATION FORMULA.
ITERATION or ITERATIVE METHOD
Literally, this means repeating a computation. Typically, it is used to find a fixed point of a function g, i.e., a solution of g(x)=x. Rootfinding and location of fixed points are equivalent problems, although the roots of one function are fixed points of a different function. If the function g has a derivative of absolute value less than 1 (and bounded away from 1) on an interval, then it is contracting on that interval. If g also maps the interval into itself, there will be a unique fixed point on the interval and every sequence of iterates starting in the interval will converge to it.
LAGRANGE INTERPOLATION FORMULA
An expression giving a polynomial of degree n using the values of the polynomial at n+1 points. The function values are multiplied by explicit polynomials that are recognized as taking the value 1 at one of the points and zero at the others.
LINEAR CONVERGENCE
Usually applied to an ITERATION. It signifies that the distance from the current approximation to the goal shrinks by a factor less than 1 at each step, in the sense that one can prove that this distance after the step is at most k times the value before for some k strictly less than 1. A consequence of this is that a fixed amount of work is needed to get an additional decimal place of accuracy. Compare to QUADRATIC CONVERGENCE.
MEAN VALUE THEOREM
An important result in Calculus that asserts that a difference quotient (which may be thought of as the slope of a chord of the graph of a function) is equal to the value of the derivative (slope of the tangent line) at some point of the interval over which the function is evaluated.
MIDPOINT RULE
A NUMERICAL INTEGRATION formula which, in its simple form, approximates the average value of a function by the value of the function at the midpoint of the interval. As a COMPOSITE INTEGRATION RULE, this is a second order rule in the sense that the error is roughly proportional to the inverse square of the number of points.
NEWTON INTERPOLATION FORMULA
An expression giving a polynomial of degree n using the values of the polynomial at n+1 points. The terms of polynomial are the products of k products of expressions of the form variable minus one of the points. This allows efficient computation, but the coefficients need to be calculated from the given data using DIVIDED DIFFERENCES.
NEWTON'S METHOD
An iterative process that finds a root of a function f described geometrically as following the tangent at a point of the graph until it meets the axis. (This leads to a simple expression, but this format does not lend itself to a nice representation of formulas.) If the graph is not tangent to the axis at the root, ITERATION of this formula exhibits QUADRATIC CONVERGENCE. It also provides an automatic way of creating a fixed point problem from a ROOTFINDING problem.
NUMERICAL DIFFERENTIATION
A procedure for approximating a derivative (possibly of high order) at a point using simpler function values (and, possibly, derivatives of lower order) at the point and a fixed set of nearby points. One way to generate these formulas, with an error term, is to start with a DIVIDED DIFFERENCE that will appear in the error term and expand it to get the desired derivative and the terms to will be used in the approximation. For example, f[x,x,y,z] expands to an expression involving f(x), f(y), f(z) and f'(x), and is usable as an error term by using the MEAN VALUE THEOREM to identify it with the value of the third derivative of f at some point in an interval containing x, y, and z. Typically, x, y, and z are equally spaced points, but a formula can be obtained that allows the positions of the points to be arbitrary.
NUMERICAL INTEGRATION
Any formula for approximating a definite integral. Familiar examples are the MIDPOINT RULE, TRAPEZOIDAL RULE, and SIMPSON'S RULE. These are originally described by approximating the average of the function defined as the integral divided by the length of the interval of integration by some numerical average of values in the interval. To get a useful approximation, one uses a COMPOSITE INTEGRATION RULE in which the interval is divided into subintervals on which a simple rule is applied. More elaborate methods designed to get higher accuracy with less effort are also available.
PARAMETRIC CURVES
A method of describing curves in the plane or space by giving each coordinate as a separate function of a single variable. If the variable represents time, this can be thought of as instructions for drawing the curve. A computationally simple method to get a good visual presentation is to divide the curve into small arcs and to use HERMITE INTERPOLATION to get third degree polynomials that match the position and tangential directions at the endpoints.
PI
The ratio of the circumference of a circle to its diameter. This one real number appears in many places, so many numerical processes reduce to finding this number.
PREDICTOR-CORRECTOR METHODS
An approach to numerical solution of initial value problems in which some initial values of the solution to the problem are used to find the quantity which the differential equation says represents the derivative of the solution. These values are used to approximate the function nearby using an interpolation formula. The integral of this approximation over an appropriate interval is then used to predict the value of the solution at a new point. The process is repeated with this predicted value to give a second approximation to the same function value.
Usually applied to an ITERATION. It signifies that the distance from the current approximation to the goal after a step is less than a fixed multiple of the square of the distance of the value before. Once the distance is small enough, each additional step gets you much closer to the goal. With a suitable scale of measurement, the number of correct digits doubles with each step. Compare to LINEAR CONVERGENCE. NEWTON'S METHOD is a familiar example. We have also met STEFFENSEN ACCELERATION as a method to produce quadratic convergence from a linearly convergent process.
ROMBERG INTEGRATION
An application of EXTRAPOLATION starting from the TRAPEZOIDAL RULE to get a chain of higher order approximations to an integral. The nature of the error terms that allows this to work is a consequence of the EULER-MACLAURIN SUMMATION formula.
ROOTFINDING
Any process that uses the ability to compute a function f to solve an equation f(x)=0. Methods described in this course are BISECTION and ITERATION.
ROUNDOFF
The error that results from representing real numbers in finite space. It typically arises when the decimal or binary representation of number is limited to a fixed accuracy to be stored in a register of a computer or calculator. It may also arise when a book has chosen to print only a few decimal places of a tabulated function. Errors caused by the inability to store intermediate results in a computation or loss of precision in computer arithmetic are often included under this term. The effect of this error is to add a small quantity to values represented by variables in formulas. Contrast with TRUNCATION error.
RUNGE-KUTTA methods
Higher order methods for solving INITIAL VALUE PROBLEMS using a sample of values of the expressions giving the derivatives of the dependent variables.
SIGNIFICANT FIGURES
A number x is said to be known to n significant figures if x is between 10k-1 and 10k and you have have an approximation to within (1/2)10k-n. (see also DECIMAL PLACES)
SIMPSON'S RULE
A NUMERICAL INTEGRATION formula which, in its simple form, approximates the average value of a function by one-sixth of the sum of the values of the function at each of the two endpoints and four times the value at the midpoint of the interval. As a COMPOSITE INTEGRATION RULE, this is a fourth order rule in the sense that the error is roughly proportional to the inverse fourth power of the number of points.
STEFFENSEN ACCELERATION
A process that derives turns a LINEARLY CONVERGENT ITERATION into a process with QUADRATIC CONVERGENCE. When you have three consecutive values from a process having linear convergence, it is possible to compute an estimate of the limit of the process. If this is used as the basis for finding three more values and the estimate of the limit, then the sequence of estimates will be quadratically convergent.
TAYLOR SERIES
A method of representing a function in terms of powers of (x-a) for some base point a. For familiar functions of calculus, this series converges in an interval (actually a disk in the complex plane) centered at a. The coefficients of the series are expressed in terms of the derivatives of the function, of all orders, at a. The initial terms of the series give a polynomial approximation to the function. The ERROR in replacing the function by this polynomial is easily estimated.
TRAPEZOIDAL RULE
A NUMERICAL INTEGRATION formula which, in its simple form, approximates the average value of a function by half the sum of the values of the function at the endpoints of the interval. As a COMPOSITE INTEGRATION RULE, this is a second order rule in the sense that the error is roughly proportional to the inverse square of the number of points.
TRUNCATION
The type of error that results from cutting off (that is what truncation means) a series expansion after a limited number of terms. This is the error term that is part of the analysis of methods that are based on series or interpolation formulas. This analysis assumes that the remaining calculations can be performed exactly, so when such formulas are used in practice, it is necessary to add another component of the error due to ROUNDOFF of data or intermediate results.

## Details

A record of the topics covered in class and homework assigned will grow in this spot.  The Topic column is usually a word that can be found in the Terminology section. The Examples column refers to textbook exercises worked in class.  They are recorded here just because it is easy to do so, and the information may be of some use.  In some cases, the discussion may be completed in a later class or with a description linked to this page. The Homework column identifies those textbook exercises  which should be done as homework.  Neatly written solutions of these problems should be submitted two class meetings after the class in which the textbook section was discussed in class.  Routine problems will be read by a grader who has been instructed to reject anything that is not clearly written. Workshop problems will be read by the lecturer. These workshop problems will be graded for organization and presentation as well as content. For routine problems, the submitted solution should be a brief report describing the method used to solve the problem and the main features of any calculation.  Any program used should be included, but a transcript of an interactive computation should be edited so that only major steps on a direct path to the answer remains.  Some of the Examples or Problems entries contain links to additional material, including tables or graphs that aim to clarify the intent of the exercise.

Lecture Homework
Date Section Topic Examples Due Page Problems
Sept. 3 2.1 BISECTION   Sept. 10   Problems 1 and 2
of Workshop 1.
Sept. 8 2.2 ITERATION   Sept. 15   Problems 1 and 2
of Workshop 2.
Sept. 10 2.3 NEWTON'S METHOD 6c Sept. 17   6a, 6d
Sept. 15 2.3, 2.4, 2.5 QUADRATIC CONVERGENCE   Sept. 29 (extra time was needed)   Problems 1 and 2
of Workshop 3.
Sept. 17 3.1 INTERPOLATION   Sept. 24   15d, 16
Sept. 22 Workshop 3 ROOTFINDING   Sept. 29 (extra time was needed)   Problems 1 and 2
of Workshop 3.
Sept. 24 1.1 TAYLOR SERIES   Oct. 01 ? ? 12
3.2 NEWTON INTERPOLATION FORMULA 6c ? ? 4
Sept. 29 Workshop 4 INTERPOLATION Section 3.1 homework. Oct. 06   Problems 1 and 2
of Workshop 4.
Oct. 01 2.5, 3.3, 3.4 DIVIDED DIFFERENCES 2.2#6, 2.5#7, 3.3#3. Oct. 08   2.5#10a; 3.3#1a, b.
Oct. 06 Workshop 4 DIVIDED DIFFERENCES #2.
3.3 HERMITE INTERPOLATION 3.3#3 (continued).
Oct. 08 3.5 PARAMETRIC CURVES   Oct. 15   1a,b.
Oct. 15 4.1 NUMERICAL DIFFERENTIATION 3a, 4a. Oct. 22   #3b and #4b, treated as a single problem; #7 using one 2 point formula, one with 3 points, and one with 5 points.
Oct. 20 Workshop 5 EXTRAPOLATION   Oct. 27   Problems 1 and 2
of Workshop 5.
Oct. 22 4.3 NUMERICAL INTEGRATION Parts a and b for problems 1-6 Oct. 29   Parts e and f for problems 1-6. (The emphasis is on the integrand not on the rule.)
Oct. 27 Workshop 6 COMPOSITE INTEGRATION RULES   Nov. 03   Problems 1 and 2
of Workshop 6.
Oct. 29 4.7 GAUSSIAN QUADRATURE 1a,2a Nov. 05   Parts c and f of #2 and #3.
Nov. 03 Workshop 7 EULER-MACLAURIN SUMMATION   Nov. 10
postponed to Nov. 12
Problems 1 and 2
of Workshop 7.
Nov. 05 5.1, 5.2 EULER'S METHOD
with Notes
5.2: 1b, 2b Nov. 12   5.2: Parts a and b of #3 and #4.
Nov. 10 Workshop 7 (revisited) EULER-MACLAURIN SUMMATION   Nov. 12   Problems 1 and 2
of Workshop 7.
5.3 TAYLOR SERIES methods Introduction: to appear in Workshop 8.
Nov. 12 Workshop 8 TAYLOR SERIES methods   Nov. 24   Problems 1 and 2
of Workshop 8.
5.4 RUNGE-KUTTA methods   Nov. 19   4b, 11b.
Nov. 17 5.5 ADAPTIVE METHODS       NONE.
Nov. 24 5.6 PREDICTOR-CORRECTOR METHODS   Dec. 03   5b, 8b.
Workshop 9 PI and the AGM   Dec. 08   Problems 1 and 2
of Workshop 9.
(see note below)

Notes on workshop 9: in response to a question (after class on Dec. 1) about problem 2a, I suggested that the wrong symbol was written in denominator. I was mistaken: the problem is correct as written. A real error was found. The quantities in problem 2c are far from equal: they are reciprocals. I was attempting to quote reference [1] of the workshop description, but I jumped to an incorrect conclusion.

This concludes the syllabus for the final exam. Other topics to be discussed in the remaining classes are an outline of advanced topics on differential equations (sections 8 through 11 of chapter 5) and splines (section 3.4).

## Study Guide

Solutions to workshops are available: workshop 1; workshop 2; workshop 3; workshop 4. The midterm exam is planned for Monday, October 13. Be sure that you bring a calculator, because some of the questions will require you to obtain numerical answers. Also, bring the textbook, since it will be an open book, but not open notes, exam. The workshop problems gave some experience with more conceptual or theoretical topics, and the exam will continue this emphasis subject to the restriction of being limited to 80 minutes. Topics covered in time to be included on the exam are all aspects of ROOTFINDING from chapter 2 and the various INTERPOLATION formulas (including TAYLOR SERIES and HERMITE INTERPOLATION). The role of DIVIDED DIFFERENCES in expressing error terms is a unifying idea. No new work will be assigned on Oct. 06 or Oct. 08 to allow concentration on reviewing for the exam, although spare time in lectures will be filled with the modification of Hermite interpolation to approximate parameterized curves (Section 3.5).

The midterm exam has been graded. The average grade was 73.04. Individual grades have been entered in the FAS Gradebook. The scatter plot shows the relation between a total of homework and workshops on the horizontal axis and the exam score on the vertical. The image also includes a regression line and a line dividing satisfactory from deficient work to aid in assigning warning grades. There is also a distribution of scores (but no attempt to assign letter grades) and scaled averages (formed my dividing by the maximum possible score [or base score ] and multiplying by 10) for each problem. Scaling allows easy comparison of the difficulty of problems of different weight

Midterm Exam
Distribution
Range Count
88 - 97 9
72 - 84 7
58 - 67 5
35 - 50 4
Problems
Prob. # Scaled
Avg.
1 8.94
2 6.00
3 8.34
4 4.54
5 7.40

More solutions to workshops are available: workshop 5; workshop 6; workshop 7; workshop 8; workshop 9.

The final exam will also require a calculator and allow the textbook, but no other resources. Exam questions will be closer to workshop problems than to the more routine calculations of the homework, but some computation will appear.

A single summary has been entered in the FAS Gradebook. The main entry is the numerical score (highest attained score was less than 400) and the comment shows how it was built from midterm, homework, workshops, and final. The workshop grade was divided by 2 to reflect its weight in the course work. The course grade also appears as part of the comment.