Awesome
poly
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Haskell library for univariate and multivariate polynomials, backed by Vector
s.
> -- Univariate polynomials
> (X + 1) + (X - 1) :: VPoly Integer
2 * X
> (X + 1) * (X - 1) :: UPoly Int
1 * X^2 + (-1)
> -- Multivariate polynomials
> (X + Y) * (X - Y) :: VMultiPoly 2 Integer
1 * X^2 + (-1) * Y^2
> (X + Y + Z) ^ 2 :: UMultiPoly 3 Int
1 * X^2 + 2 * X * Y + 2 * X * Z + 1 * Y^2 + 2 * Y * Z + 1 * Z^2
> -- Laurent polynomials
> (X^-2 + 1) * (X - X^-1) :: VLaurent Integer
1 * X + (-1) * X^-3
> (X^-1 + Y) * (X + Y^-1) :: UMultiLaurent 2 Int
1 * X * Y + 2 + 1 * X^-1 * Y^-1
Vectors
Poly v a
is polymorphic over a container v
, implementing the Vector
interface, and coefficients of type a
. Usually v
is either a boxed vector from Data.Vector
or an unboxed vector from Data.Vector.Unboxed
. Use unboxed vectors whenever possible, e. g., when the coefficients are Int
s or Double
s.
There are handy type synonyms:
type VPoly a = Poly Data.Vector.Vector a
type UPoly a = Poly Data.Vector.Unboxed.Vector a
Construction
The simplest way to construct a polynomial is using the pattern X
:
> X^2 - 3 * X + 2 :: UPoly Int
1 * X^2 + (-3) * X + 2
(Unfortunately, types are often ambiguous and must be given explicitly.)
While being convenient to read and write in REPL, X
is relatively slow. The fastest approach is to use toPoly
, providing it with a vector of coefficients (constant term first):
> toPoly (Data.Vector.Unboxed.fromList [2, -3, 1 :: Int])
1 * X^2 + (-3) * X + 2
Alternatively one can enable {-# LANGUAGE OverloadedLists #-}
and simply write
> [2, -3, 1] :: UPoly Int
1 * X^2 + (-3) * X + 2
There is a shortcut to construct a monomial:
> monomial 2 3.5 :: UPoly Double
3.5 * X^2 + 0.0 * X + 0.0
Operations
Most operations are provided by means of instances, like Eq
and Num
. For example,
> (X^2 + 1) * (X^2 - 1) :: UPoly Int
1 * X^4 + 0 * X^3 + 0 * X^2 + 0 * X + (-1)
One can also find it convenient to scale
by a monomial (cf. monomial
above):
> scale 2 3.5 (X^2 + 1) :: UPoly Double
3.5 * X^4 + 0.0 * X^3 + 3.5 * X^2 + 0.0 * X + 0.0
While Poly
cannot be made an instance of Integral
(because there is no meaningful toInteger
),
it is an instance of GcdDomain
and Euclidean
from the semirings
package. These type classes
cover the main functionality of Integral
, providing division with remainder and gcd
/ lcm
:
> Data.Euclidean.gcd (X^2 + 7 * X + 6) (X^2 - 5 * X - 6) :: UPoly Int
1 * X + 1
> Data.Euclidean.quotRem (X^3 + 2) (X^2 - 1 :: UPoly Double)
(1.0 * X + 0.0,1.0 * X + 2.0)
Miscellaneous utilities include eval
for evaluation at a given point,
and deriv
/ integral
for taking the derivative and an indefinite integral, respectively:
> eval (X^2 + 1 :: UPoly Int) 3
10
> deriv (X^3 + 3 * X) :: UPoly Double
3.0 * X^2 + 0.0 * X + 3.0
> integral (3 * X^2 + 3) :: UPoly Double
1.0 * X^3 + 0.0 * X^2 + 3.0 * X + 0.0
Deconstruction
Use unPoly
to deconstruct a polynomial to a vector of coefficients (constant term first):
> unPoly (X^2 - 3 * X + 2 :: UPoly Int)
[2,-3,1]
Further, leading
is a shortcut to obtain the leading term of a non-zero polynomial,
expressed as a power and a coefficient:
> leading (X^2 - 3 * X + 2 :: UPoly Double)
Just (2,1.0)
Flavours
-
Data.Poly
provides dense univariate polynomials with aNum
-based interface. This is a default choice for most users. -
Data.Poly.Semiring
provides dense univariate polynomials with aSemiring
-based interface. -
Data.Poly.Laurent
provides dense univariate Laurent polynomials with aSemiring
-based interface. -
Data.Poly.Sparse
provides sparse univariate polynomials with aNum
-based interface. Besides that, you may find it easier to use in the REPL because of a more readableShow
instance, skipping zero coefficients. -
Data.Poly.Sparse.Semiring
provides sparse univariate polynomials with aSemiring
-based interface. -
Data.Poly.Sparse.Laurent
provides sparse univariate Laurent polynomials with aSemiring
-based interface. -
Data.Poly.Multi
provides sparse multivariate polynomials with aNum
-based interface. -
Data.Poly.Multi.Semiring
provides sparse multivariate polynomials with aSemiring
-based interface. -
Data.Poly.Multi.Laurent
provides sparse multivariate Laurent polynomials with aSemiring
-based interface.
All flavours are available backed by boxed or unboxed vectors.
Performance
As a rough guide, poly
is at least 20x-40x faster than the polynomial
library.
Multiplication is implemented via the Karatsuba algorithm.
Here are a couple of benchmarks for UPoly Int
:
Benchmark | polynomial, μs | poly, μs | speedup |
---|---|---|---|
addition, 100 coeffs. | 45 | 2 | 22x |
addition, 1000 coeffs. | 441 | 17 | 25x |
addition, 10000 coeffs. | 6545 | 167 | 39x |
multiplication, 100 coeffs. | 1733 | 33 | 52x |
multiplication, 1000 coeffs. | 442000 | 1456 | 303x |
Due to being polymorphic by multiple axis, the performance of poly
crucially depends on specialisation of instances. Clients are strongly recommended to compile with ghc-options: -fspecialise-aggressively
and suggested to enable -O2
.