Sources of Error in Numerical Calculations

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Sources of Error in Numerical Calculations


          There are two sources of error whose effects can be measured by the bounds in this chapter: roundoff error and input error. Roundoff error arises from rounding results of floating-point operations during the algorithm. Input error is error in the input to the algorithm from prior calculations or measurements. We describe roundoff error first, and then input error.

Almost all the error bounds LAPACK provides are multiples of machine epsilon,    which we abbreviate by . Machine epsilon bounds the roundoff in individual floating-point operations. It may be loosely defined as the largest relative error    in any floating-point operation that neither overflows nor underflows. (Overflow means the result is too large to represent accurately, and underflow means the result is too small to represent accurately.) Machine epsilon is available either by the function call   SLAMCH('Epsilon') (or simply SLAMCH('E')) in single precision, or by the function call DLAMCH('Epsilon') (or DLAMCH('E')) in double precision. See section 4.1.1 and Table 4.1 for a discussion of common values of machine epsilon.      

Since overflow generally causes an error message, and underflow is almost always less significant than roundoff, we will not consider overflow and underflow further (see section 4.1.1).

Bounds on input errors, or errors in the input parameters inherited from prior computations or measurements, may be easily incorporated into most LAPACK error bounds. Suppose the input data is accurate to, say, 5 decimal digits (we discuss exactly what this means in section 4.2). Then one simply replaces by in the error bounds.

Tue Nov 29 14:03:33 EST 1994