# a blackbox solver for isolated solutions¶

The package phcpy depends on the shared object file phcpy2c.so. The module solver exports the blackbox solver of PHCpack, a fast mixed volume calculator, and several functions to predict the number of isolated solutions of a polynomial system. The test_solver() function of the module generates two trinomials (a polynomial with three monomials) with randomly generated complex coefficients.

By default, the input polynomial systems are expected to be square, that is: the number of polynomials in the input list equals the number of variables in the polynomials. The blackbox solver then returns a list of numerical approximations to the isolated solutions of the input polynomial system. Some capabilities of PHCpack to deal with positive dimensional solution sets are exported by the modules sets, cascades, factor, and diagonal. In particular, the solve() function in the factor module computes a numerical irreducible decomposition of the solution set of the polynomial system.

The first of the six subsections describes the basic application of the solve function. The output of solve is a list of strings, with each string representing a solution of the polynomial system given on input. This string representation is explained in the second subsection. The solver depends on the choice of random constants. In the third subsection, the fixing of the seed for the random number generators is demonstrated, for reproducible runs. An important aspect is the construction of a start system, which corresponds to the root counting method. Functions to count the roots in various ways are explained in the fourth subsection. In the fifth subsection, we demonstrate the application of deflation to restore the quadratic convergence of Newton’s method for isolated singularities. Equation and variable scaling improves the numerical conditioning of the solutions, as illustrated in the last subsection.

## solving random trinomials and a particular trinomial system¶

Polynomials and solutions are represented as strings. Below is an illustration of a session with the blackbox solver on a system of two random trinomials, polynomials with three monomials with random complex coefficients.

>>> from phcpy.solver import random_trinomials
>>> f = random_trinomials()
>>> for pol in f: print(pol)


To solve the system defined by f, we call the blackbox solver:

>>> from phcpy.solver import solve
>>> s = solve(f, verbose=False)
>>> len(s)
15
>>> print(s[2])
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x :  7.10290847804173E-01  -4.69841154290980E-01
y : -1.79580717006598E-01  -7.16541556066137E-01
== err :  1.986E-16 = rco :  2.676E-01 = res :  1.232E-16 =


The solve command returned a list of 15 strings in s, each string represents a solution that makes the polynomials in f vanish. The module phcpy.solutions (documented in the next section) offers a function to evaluate the solutions in the polynomials given as strings.

By default, the option verbose is set to True and the solver prints the computed root counts. The four computed root counts are

1. The total degree is the product of the degrees of the polynomials in the system.
2. The multi-homogeneous Bézout number is computed on a partition of the set of unknowns.
3. A general linear-product Bézout number leads to a start system which is a product of linear polynomials with random coefficients.
4. The mixed volume is the mixed volume of the tuple of Newton polytopes of the system. The mixed volume bounds the solutions with all coordinates different from zero. The number of all affine solutions is bounded by the stable mixed volume.

For sparse polynomial systems, the mixed volume is a generically sharp root count, i.e.: exact when the coefficients of the polynomial system are sufficiently generic.

Other options of the solver are

1. tasks: the number of tasks for multithreaded path tracking. Solving sufficiently large systems on 4 processor cores may result in a speedup of close to a factor 4 if tasks=4 is given as input argument of solve.

If you do not know how many cores are available on your computer, or if you want to double check, then you can obtain the number of available cores as follows.

>>> from phcpy.phcpy2c3 import py2c_corecount
>>> py2c_corecount()

2. precision: by default the precision is set to d for standard hardware double precision. While this precision may suffice, the blackbox solver supports two additional levels of precision: dd for double double preicsion (about 32 decimal places), and qd for quad double precision (about 64 decimal places). Given precision=dd as extra input parameter to solve is likely to yield more accurate results, at an extra cost, which may be compensated by multithreading.

3. checkin: by default this flag is set to True to check whether the system given on input has as many polynomials as variables. The current version of the blackbox solver accepts only square systems. See the section on positive dimensional solution sets for functions that deal with overdetermined or underdetermined polynomial systems.

Last and certainly not least, the first argument of solve is a list of strings. Each string in the list represents a polynomial in several variables. Consider the example below:

>>> from phcpy.solver import solve
>>> p = ['x^2*y^2 + x + 1;', 'x^2*y^2 + y + 1;']
>>> s = solve(p)
total degree : 16
2-homogeneous Bezout number : 8
with with partition : { x }{ y }
general linear-product Bezout number : 8
based on the set structure :
{ x }{ x }{ y }{ y }
{ x }{ x }{ y }{ y }
mixed volume : 4
stable mixed volume : 4
>>>


What is printed to screen by s = solve(p) is an example of the four different types of root counts. The structure of the output in s is described in the next section.

If multitasking is applied in the solver, providing a larger than one value for the option tasks, then the multi-homogeneous and the general linear-product Bézout numbers are not computed, because of the pipelined polyhedral homotopies. In pipelined polyhedral homotopies, the computation of the mixed volume is then done by one task, while the other tasks take the mixed cells and run the polyhedral homotopies to solve a random coefficient start system. This random coefficient start system will then be used to solve the given system, so there is no need for a start system based on a Bézout number.

## representations of solutions of polynomial systems¶

Solutions of phcpy.solve are returned as lists of PHCpack solution strings. The solutions module contains functions to parse a PHCpack solution string into a dictionary.

The solutions module exports operations

1. to parse strings in the PHCpack solution format into dictionaries;
2. to evaluate these dictionaries into polynomials substituting the values for the variables into the strings representing the polynomials.

The main test in the module solutions is the solution of a small trinomial system and the evaluation of the computed solutions at the trinomial system.

The information of a solution as a dictionary contains the following:

1. t : value of the continuation parameter

m : multiplicity of the solution

2. symbols for the variables are keys in the dictionary, the corresponding values are complex floating-point numbers

3. err : magnitude of the last correction term of Newton’s method (forward error)

rco : estimate for the inverse of the condition number of the Jacobian matrix at the solution

res : magnitude of the residual (backward error)

The triplet (err, rco, res) measures the numerical quality of the solution. The residual res is normally interpreted as an estimate of the backward error: by how much should we change the original problem such that the approximate solution becomes an exact solution of the changed problem. The estimate rco gives a (sometimes too pessimistic) bound on the number of correct decimal places in the approximate solution. In particular: abs(log(rco, 10)) bounds the number of lost decimal places in the approximate solution. For example, if rco equals 1.0E-8, then the last 8 decimal places in the coordinates of the solution could be wrong.

The best numerically conditioned linear systems arise when the normals to the coefficient vectors of the linear equations are perpendicular to each other, as in the next session:

>>> from phcpy.solver import solve
>>> p = ['x + y - 1;', 'x - y - 1;']
>>> s = solve(p)
>>> print s[0]
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x :  1.00000000000000E+00   0.00000000000000E+00
y :  0.00000000000000E+00  -0.00000000000000E+00
== err :  2.220E-16 = rco :  5.000E-01 = res :  0.000E+00 =


The value of rco is 5.0E-1 which implies that the condition number is bounded by 2, as rco is an estimate for the inverse of the condition number. Roundoff errors are doubled at most.

At the opposite end of the best numerically conditioned linear systems are those where the the normals to the coefficient vectors of the linear equations are almost parallel to each other, as illustrated in the next example:

>>> from phcpy.solver import solve
>>> p = ['x + y - 1;', 'x + 0.999*y - 1;']
>>> s = solve(p)
>>> print s[0]
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x :  1.00000000000000E+00   0.00000000000000E+00
y :  0.00000000000000E+00  -0.00000000000000E+00
== err :  2.220E-16 = rco :  2.501E-04 = res :  0.000E+00 =


The reported estimate for the inverse of the condition number rco is 2.5E-4, which implies that the condition number is estimated at 4,000. Thus for this example, roundoff errors may magnify thousandfold. In the next example, the condition number becomes a 10-digit number:

>>> from phcpy.solver import solve
>>> p = ['x + y - 1;', 'x + 0.999999999*y - 1;']
>>> s = solve(p)
>>> print s[0]
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x :  1.00000000000000E+00   0.00000000000000E+00
y :  0.00000000000000E+00  -0.00000000000000E+00
== err :  2.220E-16 = rco :  2.500E-10 = res :  0.000E+00 =


Note that the actual value of the solution remains (1,0), which on the one hand indicates that the condition number is a pessimistic bound on the accuracy of the solution. But on the other hand, (1,0) may give the false security that the solution is right, because the problem on input is very close to a linear system which has infinitely many solutions (the line x + y - 1 = 0) and not the isolated point (1,0).

For a solution of the example noon3 from the module examples, we convert the PHCpack format solution string to a dictionary as follows:

>>> print(s[0])
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x1 : -1.65123467890611E-01  -7.61734168646636E-01
x2 :  8.98653694263692E-01  -3.48820047576431E-01
x3 :  8.98653694263692E-01  -3.48820047576431E-01
== err :  3.034E-16 = rco :  2.761E-01 = res :  5.974E-16 =
>>> from phcpy.solutions import strsol2dict
>>> d = strsol2dict(s[0])
>>> d.keys()
['err', 'res', 'm', 'rco', 't', 'x2', 'x3', 'x1']
>>> d['x1']
(-0.165123467890611-0.761734168646636j)


Note that the values of the dictionary d are evaluated strings, parsed into Python objects.

By plain substitution of the values of the dictionary representation of the solution into the string representation of the polynomial system we can verify that the coordinates of the solution evaluate to numbers close to the numerical working precision:

>>> from phcpy.solutions import evaluate
>>> e = evaluate(f,d)
>>> for x in e: print(x)
...
(1.11022302463e-15+4.4408920985e-16j)
(7.77156117238e-16+9.99200722163e-16j)
(7.77156117238e-16+9.99200722163e-16j)


A more elaborate verification of the solution is provided by the function newton_step of the module solver of phcpy.

The module exports function to filter regular solutions, solutions with zero coordinates or real solutions. The filtering of real solutions is illustrated in the session below. We first define one real solution and another with a coordinate that has a nonzero imaginary part.

>>> from phcpy.solutions import make_solution
>>> s0 = make_solution(['x', 'y'], [complex(1, 0), complex(0, 1)])
>>> print(s0)
t : 0.0 0.0
m : 1
the solution for t :
x : 1.000000000000000E+00  0.0
y : 0.000000000000000E+00  1.000000000000000E+00
== err : 0.0 = rco : 1.0 = res : 0.0 ==
>>> s1 = make_solution(['x', 'y'], [float(2), float(3)])
>>> print(s1)
t : 0.0 0.0
m : 1
the solution for t :
x : 2.000000000000000E+00  0.0
y : 3.000000000000000E+00  0.0
== err : 0.0 = rco : 1.0 = res : 0.0 ==


The filtering of real solutions (with respect to a given tolerance) is provided by the functions is_real (on one solution) and filter_real (on a list of solutions).

>>> from phcpy.solutions import is_real, filter_real
>>> is_real(s0, 1.0e-8)
False
>>> is_real(s1, 1.0e-8)
True
>>> realsols = filter_real([s0, s1], 1.0e-8, 'select')
>>> for sol in realsols: print(sol)
...
t : 0.0 0.0
m : 1
the solution for t :
x : 2.000000000000000E+00  0.0
y : 3.000000000000000E+00  0.0
== err : 0.0 = rco : 1.0 = res : 0.0 ==


The functions filter_regular and filter_zero_coordinates operate in a manner similar as filter_real.

Another application of make_solution is to turn the solution at the end of path (with value 1.0 for t) to a solution which can serve at the start of another path (with value 0.0 for t). This is illustrated in the session below. We start by solving a simple system.

>>> from phcpy.solver import solve
>>> p = ['x**2 - 3*y + 1;', 'x*y - 3;']
>>> s = solve(p, verbose=False)
>>> print(s[0])
t :  1.00000000000000E+00   1.14297839516487E+00
m : 1
the solution for t :
x :  1.92017512134718E+00   0.00000000000000E+00
y :  1.56235749888022E+00   9.27337524477545E-124
== err :  2.738E-16 = rco :  2.976E-01 = res :  4.441E-16 =


Then we import the functions coordinates and make_solution of the module solutions.

>>> from phcpy.solutions import coordinates, make_solution
>>> (names, values) = coordinates(s[0])
>>> names
['x', 'y']
>>> values
[(1.92017512134718+0j), (1.56235749888022+9.27337524477545e-124j)]
>>> s0 = make_solution(names, values)
>>> print(s0)
t : 0.0 0.0
m : 1
the solution for t :
x : 1.920175121347180E+00  0.000000000000000E+00
y : 1.562357498880220E+00  9.273375244775450E-124
== err : 0.0 = rco : 1.0 = res : 0.0 ==


Observe that also the diagnostics are set to the defaults.

## reproducible runs with fixed seeds¶

The solver in PHCpack generates different random numbers with each run, which may very well cause the solutions to appear in a different order after a second application of solve on the same system. To prevent this behaviour (to check reproducibility for example), we can fix the seed of the random number generators in PHCpack, as follows:

>>> from phcpy.phcpy2c3 import py2c_set_seed
>>> py2c_set_seed(2013)
0


The above session continues as

>>> from phcpy.phcpy2c3 import py2c_get_seed
>>> py2c_get_seed()
2013


To reproduce a computation, we can thus request the seed that was used (with py2c_get_seed) and then restart the session setting the seed to what was used before (with py2c_set_seed).

## root counting methods¶

The performance of the solver is very sensitive to how accurately we can predict the number of solutions. For dense polynomial systems, looking at the highest degrees of the polynomials in the system suffices, whereas for sparse polynomial systems, computing the mixed volume of the Newton polytopes of the polynomials yields much better results. Below is a simple example, illustrating the bounds based on the degrees and the mixed volume:

>>> f = ['x^3*y^2 + x*y^2 + x^2;', 'x^5 + x^2*y^3 + y^2;']
>>> from phcpy.solver import total_degree
>>> total_degree(f)
25
>>> from phcpy.solver import m_homogeneous_bezout_number as mbz
>>> mbz(f)
(19, '{ x }{ y }')
>>> from phcpy.solver import linear_product_root_count as lrc
>>> lrc(f)
a supporting set structure :
{ x }{ x }{ x }{ y }{ y }
{ x }{ x }{ x y }{ x y }{ x y }
the root count : 19
19
>>> from phcpy.solver import mixed_volume
>>> mixed_volume(f, stable=True)
(14, 18)


The mixed volume is a generically sharp root count for the number of isolated solutions with all coordinates different from zero. The term generically sharp means: except for systems with coefficients in a specific collection of algebraic sets, the root count is an exact count. The stable mixed volume counts all affine solutions, that is: also the solutions with zero coordinates. For the example above, we may expect at most 14 isolated solutions with all coordinates different from zero, and, also considering solutions with zero coordinates, at most 18 isolated solutions, counted with multiplicities.

For larger polynomial systems with many different supports, DEMiCs is faster than MixedVol. The code snippet below illustrates the computation of the mixed volume by calling DEMiCs.

>>> f = ['x^3*y^2 + x*y^2 + x^2;', 'x^5 + x^2*y^3 + y^2;']
>>> from phcpy.solver import mixed_volume_by_demics as demics
>>> demics(f)
14


For every root count, total degree, m-homogeneous Bézout number, linear-product root count, and mixed volume, there is a corresponding method to construct a polynomial system with exactly as many regular solutions at the root count, which can then be used as a start system in a homotopy to compute all isolated solutions of the polynomial system for which the root count was computed. Examples of the methods to construct start systems in phcpy are illustrated in the documentation for the module phcpy.trackers.

## Newton’s method and deflation¶

Newton’s method fails when the Jacobian matrix is singular (or close to singular) at a solution. Below is a session on the example of A. Griewank and M. R. Osborne, in their paper Analysis of Newton’s method at irregular singularities, published in SIAM J. Numer. Anal. 20(4): 747-773, 1983. The origin (0,0) is an irregular singularity: Newton’s method fails no matter how close the initial guess is taken. With deflation we can restore the quadratic convergence of Newton’s method:

>>> p = ['(29/16)*x^3 - 2*x*y;', 'x^2 - y;']
>>> from phcpy.solutions import make_solution
>>> s = make_solution(['x', 'y'], [float(1.0e-6), float(1.0e-6)])
>>> print(s)
t : 0.0 0.0
m : 1
the solution for t :
x : 1.000000000000000E-06  0.0
y : 1.000000000000000E-06  0.0
== err : 0.0 = rco : 1.0 = res : 0.0 ==
>>> from phcpy.solver import newton_step
>>> s2 = newton_step(p,[s])
== err :  1.000E-06 = rco :  5.625E-13 = res :  1.875E-19 =
>>> print(s2[0])
t :  0.00000000000000E+00   0.00000000000000E+00
m : 0
the solution for t :
x :  9.99999906191101E-07   0.00000000000000E+00
y :  9.99999812409806E-13   0.00000000000000E+00
== err :  1.000E-06 = rco :  5.625E-13 = res :  1.875E-19 =
>>> s3 = newton_step(p,s2)
== err :  3.333E-07 = rco :  2.778E-14 = res :  1.111E-13 =
>>> print(s3[0])
t :  0.00000000000000E+00   0.00000000000000E+00
m : 0
the solution for t :
x :  6.66666604160106E-07   0.00000000000000E+00
y :  3.33333270859482E-13   0.00000000000000E+00
== err :  3.333E-07 = rco :  2.778E-14 = res :  1.111E-13 =
>>> from phcpy.solver import standard_deflate
>>> sd = standard_deflate(p,[s])
>>> print(sd[0])
t :  0.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x : -4.55355758042535E-25   2.75154683741089E-26
y :  1.57904709676279E-25  -8.86785799319512E-26
== err :  5.192E-13 = rco :  5.314E-03 = res :  1.388E-16 =


The decision to deflate or not depend on the tolerance to decide the numerical rank. Consider the following session:

from phcpy.solutions import make_solution
from phcpy.solver import standard_deflate
sol = make_solution(['x', 'y'], [float(1.0e-6), float(1.0e-6)])
print(sol)
pols = ['x**2;', 'x*y;', 'y**2;']
sols = standard_deflate(pols, [sol], tolrnk=1.0e-8)
print(sols[0])
sols = standard_deflate(pols, [sol], tolrnk=1.0e-4)
print(sols[0])


The default value for tolrnk equals 1.0e-6. If we do not want to deflate that soon, we can lower the tolerance to 1.0e-8 and in that case, there is no deflation when the approximation is still as far as 1.0e-6 from the exact solution. Increasing the value for the tolerance to 1.0e-4 leads to the deflation at the approximation for the solution.

## the multiplicity of an isolated solution¶

The multiplicity of an isolated solution can be computed following the ISSAC 2005 paper by Barry Dayton and Zhonggang Zeng on Computing the multiplicity structure in solving polynomial systems. Consider again the example of the Griewank-Osborne paper of the previous section:

p = ['(29/16)*x^3 - 2*x*y;', 'x^2 - y;']
from phcpy.solutions import make_solution
s = make_solution(['x', 'y'], [0.0, 0.0])
from phcpy.solver import standard_multiplicity as multip
print(multip(p,s))


The outcome of the commands above is 3, which corresponds to the multiplicity of the isolated solution.

The default value for order equals 5, where order is the maximal differentiation order. If order is too small, then the value on return may be strict lower bound on the multiplicity. Making order too large may exhaust the stack size. The default value for the tolerance on the numerical rank is 1.0e-8 and by default verbose is set to False.

With dobldobl_multiplicity and quaddobl_multiplicity computations happen respectively in double double and quad double precision.

## equation and variable scaling¶

Another source of numerical difficulties are systems that have extreme values as coefficients. With equation and variable scaling we solve an optimization problem to find coordinate transformations that lead to better values for the coefficients. The common sense approach to scaling is described in Chapter 5 of the book of Alexander Morgan on Solving Polynomial Systems Using Continuation for Engineering and Scientific Problems, volume 57 in the SIAM Classics in Applied Mathematics, 2009. We consider a simple example.

>>> from phcpy.solver import solve
>>> p = ['0.000001*x^2 + 0.000004*y^2 - 4;', '0.000002*y^2 - 0.001*x;']
>>> psols = solve(p, verbose=False)
>>> print(psols[0])
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x : -3.23606797749979E+03   8.71618409420601E-19
y :  2.30490982555757E-19   1.27201964951407E+03
== err :  2.853E-07 = rco :  2.761E-04 = res :  9.095E-13 =


Observe the rather large values of the coordinates in the first solution and the estimate for the inverse condition number. We scale the system as follows:

>>> from phcpy.solver import standard_scale_system as scalesys
>>> from phcpy.solver import standard_scale_solutions as scalesols
>>> (q, c) = scalesys(p)
>>> q[0]
'x^2 + 9.99999999999998E-01*y^2 - 1.00000000000000E+00;'
>>> q[1]
'y^2 - 1.00000000000000E+00*x;'


The coefficients in the scaled system look indeed a lot nicer. In the parameter c returned along with the scaled system are the scaling coefficients, which we need to bring the solutions of the scaled system into the original coordinates.

>>> qsols = solve(q, verbose=False)
>>> ssols = scalesols(len(q), qsols, c)
>>> for sol in ssols: print(sol)
...
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x : -3.23606797749978E+03  -1.98276706040285E-115
y :  0.00000000000000E+00  -1.27201964951407E+03
== err :  1.746E-16 = rco :  2.268E-01 = res :  2.220E-16 =
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x : -3.23606797749978E+03  -1.98276706040285E-115
y :  0.00000000000000E+00   1.27201964951407E+03
== err :  1.746E-16 = rco :  2.268E-01 = res :  2.220E-16 =
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x :  1.23606797749979E+03   0.00000000000000E+00
y :  7.86151377757423E+02   0.00000000000000E+00
== err :  4.061E-17 = rco :  4.601E-01 = res :  5.551E-17 =
t :  1.00000000000000E+00   0.00000000000000E+00
m : 1
the solution for t :
x :  1.23606797749979E+03   0.00000000000000E+00
y : -7.86151377757423E+02   7.38638289422858E-124
== err :  4.061E-17 = rco :  4.601E-01 = res :  5.551E-17 =


The estimates of the condition numbers in ssols are for the scaled problem. With scaling, the condition numbers were reduced from 10^4 to 10. For more extreme values of the coefficients, we may have to perform the scaling in higher precision, such as available in the functions dobldobl_scale_system and quaddobl_scale_system, respectively with double double and quad double arithmetic.

## reduction of polynomial systems¶

Applying row reduction on the coefficient matrix of a polynomial system may lead to a system with fewer monomials and a lower root count. Consider for example the following session:

>>> p = ['x**2*y**2 + x + 1;', 'x**2*y**2 + y + 1;']
>>> from phcpy.solver import linear_reduce
>>> r = linear_reduce(p)
>>> for pol in r: print(pol)


The printed polynomials are x^2*y^2 + y + 1; and + x - y; showing that, while the system is invariant under swapping of x and y, all solutions are fixed points as both coordinates for all four solutions will be the same.

The precision of the row reduction is increased to double double by providing the argument precision='dd' and to quad double via the argument precision='qd'.

Nonlinear reduction computes S-polynomials to eliminate the leading term and then, if a criterion with R-polynomials is satisfied, replaces one of the polynomials in the system by the S-polynomial. Consider the session below:

>>> from phcpy.solver import standard_nonlinear_reduction as reduce
>>> pols = ['x^3 - x;', 'x^2*y + 1;']
>>> redu = reduce(pols)
number of equal degree replacements : 5
number of computed S-polynomials : 9
number of computed R-polynomials : 16
>>> for pol in redu: print(pol)


What is printed are the polynomials + y + 1; and + x^2 - 1; which allows to read off the solutions.