Comments (2)
So, Polynomial.roots
will not be suitable for degrees above 50 or so, if even. The AMRVW
package can do much better, but for your problem a specialized algorithm is likely the way to go. The RealPolynomialRoots
package is intended to this, but doesn't scale to large polynomials well, as it doesn't have some more asymptotically efficient algorithms implemented (just naive ones). So... I'd suggest trying Nemo (https://nemocas.github.io/Nemo.jl/v0.13/polynomial.html#Root-finding-1) which uses the arb library under the hood and has the isolating algorithms you likely need.
from polynomials.jl.
Aha, thank you very much!
from polynomials.jl.
Related Issues (20)
- docs: advertise composition of polynomials HOT 1
- `ImmutablePolynomial` constructors seem to prevent good type inference HOT 4
- improved implementations of composition of polynomials HOT 4
- bad type inference for composition of `ImmutablePolynomial`s HOT 2
- Should `AbstractPolynomial` subtype `Function`? HOT 2
- Question: is multipliciation of polynomials implemented with FFT HOT 6
- type stability: `(::Polynomial)(::ImmutablePolynomial)` composition yields `ImmutablePolynomial` HOT 2
- polynomials should have `big` methods? HOT 2
- Should `coeffs(::ImmutablePolynomial)` be type-stable? HOT 1
- Feature request: decompose a polynomial HOT 1
- Polynomials.fit fails with InexactError for Rational HOT 2
- Warnings and Test failure with Julia 1.10 HOT 2
- Polynomial/Polynomial = Rational or? HOT 1
- (Low priority) Axis offset warning when using OffsetArrays HOT 3
- Conversions to and from RationalFunction broken HOT 3
- MakieCore doesn't need to be in `deps` HOT 12
- Issues with differentiating with respect to polynomial coefficients
- Polynomials.fit changes behavior somewhere between (inclusive) Julia 1.7.3 and Julia 1.10.4, the latter being self-inconsistent (problematic)
- Polynomials requiring MakieCore HOT 5
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from polynomials.jl.