jax.numpy.polymul#

jax.numpy.polymul(a1,a2,*,trim_leading_zeros=False)[source]#

Returns the product of two polynomials.

JAX implementation ofnumpy.polymul().

Parameters:

• a1 (ArrayLike) – 1D array of polynomial coefficients.

• a2 (ArrayLike) – 1D array of polynomial coefficients.

• trim_leading_zeros (bool) – Default isFalse. IfTrue removes the leadingzeros in the return value to match the result of numpy. But prevents thefunction from being able to be used in compiled code. Due to differencesin accumulation of floating point arithmetic errors, the cutoff for valuesto be considered zero may lead to inconsistent results between NumPy andJAX, and even between different JAX backends. The result may lead toinconsistent output shapes whentrim_leading_zeros=True.

Returns:

An array of the coefficients of the product of the two polynomials. The dtypeof the output is always promoted to inexact.

Return type:

Array

Note

jax.numpy.polymul() only accepts arrays as input unlikenumpy.polymul() which accepts scalar inputs as well.

See also

• jax.numpy.polyadd(): Computes the sum of two polynomials.

• jax.numpy.polysub(): Computes the difference of two polynomials.

• jax.numpy.polydiv(): Computes the quotient and remainder of polynomialdivision.

Examples

>>>x1=np.array([2,1,0])>>>x2=np.array([0,5,0,3])>>>np.polymul(x1,x2)array([10,  5,  6,  3,  0])>>>jnp.polymul(x1,x2)Array([ 0., 10.,  5.,  6.,  3.,  0.], dtype=float32)

Iftrim_leading_zeros=True, the result matches withnp.polymul’s.

>>>jnp.polymul(x1,x2,trim_leading_zeros=True)Array([10.,  5.,  6.,  3.,  0.], dtype=float32)

For input arrays of dtypecomplex:

>>>x3=np.array([2.,1+2j,1-2j])>>>x4=np.array([0,5,0,3])>>>np.polymul(x3,x4)array([10. +0.j,  5.+10.j, 11.-10.j,  3. +6.j,  3. -6.j])>>>jnp.polymul(x3,x4)Array([ 0. +0.j, 10. +0.j,  5.+10.j, 11.-10.j,  3. +6.j,  3. -6.j],      dtype=complex64)>>>jnp.polymul(x3,x4,trim_leading_zeros=True)Array([10. +0.j,  5.+10.j, 11.-10.j,  3. +6.j,  3. -6.j], dtype=complex64)