Deconvolution and polynomial division
deconvolves a vector
v out of a vector
long division, and returns the quotient
q and remainder
r such that
u = conv(v,q) + r. If
v are vectors of polynomial
coefficients, then deconvolving them is equivalent to dividing the polynomial
u by the polynomial represented by
Create two vectors
v containing the coefficients of the polynomials and , respectively. Divide the first polynomial by the second by deconvolving
v out of
u, which results in quotient coefficients corresponding to the polynomial and remainder coefficients corresponding to .
u = [2 7 4 9]; v = [1 0 1]; [q,r] = deconv(u,v)
q = 1×2 2 7
r = 1×4 0 0 2 2
u,v — Input vectors
row or column vectors
Input vectors, specified as either row or column vectors.
v can be different lengths or data types.
If one or both of
vare of type single, then the output is also of type single. Otherwise,
The lengths of the inputs should generally satisfy
length(v) <= length(u). However, if
length(v) > length(u), then
deconvreturns the outputs as
q = 0and
r = u.
Complex Number Support: Yes
q — Quotient
row or column vector
Quotient, returned as a row or column vector such that
r — Remainder
row or column vector
Remainder, returned as a row or column vector such that
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
See Variable-Sizing Restrictions for Code Generation of Toolbox Functions (MATLAB Coder).
Run code in the background using MATLAB®
backgroundPool or accelerate code with Parallel Computing Toolbox™
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.
This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).