NAG Library Routine Document

c02aff (poly_complex)


    1  Purpose
    7  Accuracy


c02aff finds all the roots of a complex polynomial equation, using a variant of Laguerre's method.


Fortran Interface
Subroutine c02aff ( a, n, scal, z, w, ifail)
Integer, Intent (In):: n
Integer, Intent (Inout):: ifail
Real (Kind=nag_wp), Intent (In):: a(2,n+1)
Real (Kind=nag_wp), Intent (Out):: z(2,n), w(4*(n+1))
Logical, Intent (In):: scal
C Header Interface
#include nagmk26.h
void  c02aff_ (const double a[], const Integer *n, const logical *scal, double z[], double w[], Integer *ifail)


c02aff attempts to find all the roots of the nth degree complex polynomial equation
Pz = a0 zn + a1 zn-1 + a2 zn-2 + + an-1 z + an = 0 .  
The roots are located using a modified form of Laguerre's method, originally proposed by Smith (1967).
The method of Laguerre (see Wilkinson (1965)) can be described by the iterative scheme
Lzk = zk+1 - zk = -n P zk P zk ± Hzk ,  
where Hzk=n-1n-1Pzk2-nPzkPzk and z0 is specified.
The sign in the denominator is chosen so that the modulus of the Laguerre step at zk, viz. Lzk, is as small as possible. The method can be shown to be cubically convergent for isolated roots (real or complex) and linearly convergent for multiple roots.
The routine generates a sequence of iterates z1,z2,z3,, such that Pzk+1<Pzk and ensures that zk+1+Lzk+1 ‘roughly’ lies inside a circular region of radius F about zk known to contain a zero of Pz; that is, Lzk+1F, where F denotes the Fejér bound (see Marden (1966)) at the point zk. Following Smith (1967), F is taken to be minB,1.445nR , where B is an upper bound for the magnitude of the smallest zero given by
B = 1.0001 × min n L zk ,r1, an / a0 1/n ,  
r1 is the zero X of smaller magnitude of the quadratic equation
P zk 2 n n-1 X2 + P zk n X + 12 Pzk = 0  
and the Cauchy lower bound R for the smallest zero is computed (using Newton's Method) as the positive root of the polynomial equation
a0 zn + a1 zn-1 + a2 zn-2 ++ an-1 z - an = 0 .  
Starting from the origin, successive iterates are generated according to the rule zk+1 = zk + L zk , for k=1 , 2 , 3 ,, and Lzk is ‘adjusted’ so that P zk+1 < P zk  and L zk+1 F . The iterative procedure terminates if P zk+1  is smaller in absolute value than the bound on the rounding error in P zk+1  and the current iterate zp = zk+1  is taken to be a zero of Pz. The deflated polynomial P~ z = P z / z-zp  of degree n-1 is then formed, and the above procedure is repeated on the deflated polynomial until n<3, whereupon the remaining roots are obtained via the ‘standard’ closed formulae for a linear (n=1) or quadratic (n=2) equation.
Note that c02ahf, c02amf and c02anf can be used to obtain the roots of a quadratic, cubic (n=3) and quartic (n=4) polynomial, respectively.


Marden M (1966) Geometry of polynomials Mathematical Surveys 3 American Mathematical Society, Providence, RI
Smith B T (1967) ZERPOL: a zero finding algorithm for polynomials using Laguerre's method Technical Report Department of Computer Science, University of Toronto, Canada
Thompson K W (1991) Error analysis for polynomial solvers Fortran Journal (Volume 3) 3 10–13
Wilkinson J H (1965) The Algebraic Eigenvalue Problem Oxford University Press, Oxford


1:     a2n+1 – Real (Kind=nag_wp) arrayInput
On entry: if a is declared with bounds 2,0:n, a1i and a2i must contain the real and imaginary parts of ai (i.e., the coefficient of zn-i), for i=0,1,,n.
Constraint: a10 0.0  or a20 0.0 .
2:     n – IntegerInput
On entry: n, the degree of the polynomial.
Constraint: n1.
3:     scal – LogicalInput
On entry: indicates whether or not the polynomial is to be scaled. See Section 9 for advice on when it may be preferable to set scal=.FALSE. and for a description of the scaling strategy.
Suggested value: scal=.TRUE..
4:     z2n – Real (Kind=nag_wp) arrayOutput
On exit: the real and imaginary parts of the roots are stored in z1i  and z2i  respectively, for i=1,2,,n.
5:     w4×n+1 – Real (Kind=nag_wp) arrayWorkspace
6:     ifail – IntegerInput/Output
On entry: ifail must be set to 0, -1​ or ​1. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ or ​1 is recommended. If the output of error messages is undesirable, then the value 1 is recommended. Otherwise, if you are not familiar with this argument, the recommended value is 0. When the value -1​ or ​1 is used it is essential to test the value of ifail on exit.
On exit: ifail=0 unless the routine detects an error or a warning has been flagged (see Section 6).

Error Indicators and Warnings

If on entry ifail=0 or -1, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
On entry,a10=0.0 and a20=0.0,
The iterative procedure has failed to converge. This error is very unlikely to occur. If it does, please contact NAG, as some basic assumption for the arithmetic has been violated. See also Section 9.
Either overflow or underflow prevents the evaluation of Pz near some of its zeros. This error is very unlikely to occur. If it does, please contact NAG. See also Section 9.
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.


All roots are evaluated as accurately as possible, but because of the inherent nature of the problem complete accuracy cannot be guaranteed. See also Section 10.

Parallelism and Performance

c02aff is not threaded in any implementation.

Further Comments

If scal=.TRUE., then a scaling factor for the coefficients is chosen as a power of the base b of the machine so that the largest coefficient in magnitude approaches thresh=bemax-p. You should note that no scaling is performed if the largest coefficient in magnitude exceeds thresh, even if scal=.TRUE.. (b, emax and p are defined in Chapter X02.)
However, with scal=.TRUE., overflow may be encountered when the input coefficients a0,a1,a2,,an vary widely in magnitude, particularly on those machines for which b4p  overflows. In such cases, scal should be set to .FALSE. and the coefficients scaled so that the largest coefficient in magnitude does not exceed b emax-2p .
Even so, the scaling strategy used by c02aff is sometimes insufficient to avoid overflow and/or underflow conditions. In such cases, you are recommended to scale the independent variable z so that the disparity between the largest and smallest coefficient in magnitude is reduced. That is, use the routine to locate the zeros of the polynomial dPcz for some suitable values of c and d. For example, if the original polynomial was Pz=2-100i+2100z20, then choosing c=2-10 and d=2100, for instance, would yield the scaled polynomial i+z20, which is well-behaved relative to overflow and underflow and has zeros which are 210 times those of Pz.
If the routine fails with ifail=2 or 3, then the real and imaginary parts of any roots obtained before the failure occurred are stored in z in the reverse order in which they were found. Let nR  denote the number of roots found before the failure occurred. Then z1n  and z2n  contain the real and imaginary parts of the first root found, z1n-1  and z2n-1  contain the real and imaginary parts of the second root found, , z1 n- nR+1  and z2 n- nR+1  contain the real and imaginary parts of the nR th root found. After the failure has occurred, the remaining 2× n-nR  elements of z contain a large negative number (equal to -1 / x02amf×2 ).


For this routine two examples are presented. There is a single example program for c02aff, with a main program and the code to solve the two example problems given in the subroutines EX1 and EX2.
Example 1 (EX1)
This example finds the roots of the polynomial
a0 z5 + a1 z4 + a2 z3 + a3 z2 + a4 z+a5 = 0 ,  
where a0=5.0+6.0i , a1=30.0+20.0i , a2=- 0.2+6.0i , a3=50.0+100000.0i , a4=- 2.0-40.0i  and a5=10.0+1.0i .
Example 2 (EX2)
This example solves the same problem as subroutine EX1, but in addition attempts to estimate the accuracy of the computed roots using a perturbation analysis. Further details can be found in Thompson (1991).

Program Text

Program Text (c02affe.f90)

Program Data

Program Data (c02affe.d)

Program Results

Program Results (c02affe.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017