If phcpack does not report any 'failure' solutions in
classified_solution_dicts(), then it should have found all the roots.
But the groebner methods are preferable if you are interested in exact
solutions.

-Marshall

On Jul 16, 11:29 am, Doug <mcke...@gmail.com> wrote:
> > The last element of that Groebner basis is a univariate polynomial in
> > x2, so it is relatively easy to analyze its solutions.  There is one
> > triple root at x2=0, and then four others which are presumably the
> > four you are thinking of.  Once you have a value for x2, you can
> > substitute it into the other Groebner basis equations to find x1.
>
> Indeed, you're right, my preferred solution is in there!  I looked to
> see if the first element was in one variable and when it wasn't, I
> didn't think to look at the others.  doh!  Now, I can copy and paste
> the appropriate basis element  to turn it into something I can pass to
> solve(), but there must be a better (more programmatic) way to coerce
> it:
>
> sage: B[2]
> x2^7 + 4*x2^6 - 6*x2^5 - 20*x2^4 + 5*x2^3
> sage: type(B[2])
> <type
> 'sage.rings.polynomial.multi_polynomial_libsingular.MPolynomial_libsingular'>
> sage: foo = x2^7 + 4*x2^6 - 6*x2^5 - 20*x2^4 + 5*x2^3 ; foo
> x2^7 + 4*x2^6 - 6*x2^5 - 20*x2^4 + 5*x2^3
> sage: type(foo)
> <type 'sage.symbolic.expression.Expression'>
>
> > ... phcpack ...
>
> My equations are actually first order condtions of the Lagrangian from
> a constrained optimization problem.  I've been trying to solve them
> symbolically so I know I've found all the solutions (not just the one
> closest to my starting value).  That is, up til now I've been loath to
> apply numerical optimization methods to the problem directly.  But
> using phcpack on the FOC's seems to find all the solutions so that
> might be a numerical method that's OK in this case.
>
> Thanks!
>
> Doug
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