One of the great attractions of mathematics, to its devotees, may be that a well posed problem does in fact have
a unique and exact solution. This idea of absolute precision has a sort of beauty that in its own way is
unsurpassable. It may also be what frightens away many who feel more comfortable where there are shades of
gray. In numerical analysis we enjoy a little bit of both worlds but are much closer to the former than the latter.
The perfect solution is definitely out there somewhere in thought space, and knowledge of this fact has
sustained many a wearisome effort to get close to it. In what direction does it lie? How close to it are we now?
In the year 1225 Leonardo of Pisa studied the equation
x^3 + 2x^2 + 10x - 20 = 0
seeking its one real root, and produced x = 1.368808107. Nobody knows his method, but it is a remarkable
achievement for his time. Leonardo surely knew it was not perfection and may have wondered at the true
identity of the target number, but must have derived great pleasure from realizing how close he had come.
Numerical analysis problems do have exact solutions, but the thrill of victory does not wait for their discovery. It
is enough to come close. Error is expected. Without it we would be out of business. There is no need of
approximation where the real thing is within grasp. The following problems illustrate mathematics with some
controlled shades of gray. It has been a pleasure to work them through. I was almost sorry to come to the 2000th.
Though error is the substance of our subject, mistakes and blunders are not. I hope there are none, but experience
suggests otherwise. I will be grateful to anyone who takes the time to point them out, in the kinder and gentler way
that my now advanced years can withstand.
Author(s): Francis Scheid
Series: Schaum's Solved Problems Series
Edition: 1
Publisher: McGraw-Hill Publishing Company
Year: 1990
Language: English
Pages: 698
City: New York
Tags: Numerical Analysis
Cover
CHAPTER 1 The Representation of Numbers
CHAPTER 2 Algorithms and Error
CHAPTER 3 Classical Numerical Analysis to Newton's Formula
CHAPTER 4 Classical Numerical Analysis: Further Developments
CHAPTER 5 Higher-Order Approximations
CHAPTER 6 Interpolation and Prediction
CHAPTER 7 Numerical Differentiation
CHAPTER 8 Numerical Integration
CHAPTER 9 Sums and Series
CHAPTER 10 Difference Equations
CHAPTER 11 Differential Equations
CHAPTER 12 Least-Squares Polynomial Approximation
CHAPTER 13 Min-Max and L1 Polynomial Approximation
CHAPTER 14 Approximation by Rational Functions
CHAPTER 15 Trigonometric Approximation
CHAPTER 16 Roots of Equations
CHAPTER 17 Linear Systems
CHAPTER 18 Optimization
CHAPTER 19 Overdetermined Systems
CHAPTER 20 Boundary Value Problems
CHAPTER 21 Monte Carlo Methods
Index