deconv

Deconvolution and polynomial division

Syntax

[q,r] = deconv(u,v)

Description

[q,r] = deconv(u,v) deconvolves a vector v out of a vector u using long division, and returns the quotient q and remainder r such that u = conv(v,q) + r. If u and v are vectors of polynomial coefficients, then deconvolving them is equivalent to dividing the polynomial represented by u by the polynomial represented by v.

Examples

Polynomial Division

Create two vectors u and v containing the coefficients of the polynomials $$2x^3+7x^2+4x+9$$ and $$x^2+1$$, respectively. Divide the first polynomial by the second by deconvolving v out of u, which results in quotient coefficients corresponding to the polynomial $$2x+7$$ and remainder coefficients corresponding to $$2x+2$$.

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

Input Arguments

u,v — Input vectors

row or column vectors

Input vectors, specified as either row or column vectors. u and v can be different lengths or data types.

• If one or both of u and v are of type single, then the output is also of type single. Otherwise, deconv returns type double.

• The lengths of the inputs should generally satisfy length(v) <= length(u). However, if length(v) > length(u), then deconv returns the outputs as q = 0 and r = u.

Data Types: double | single
Complex Number Support: Yes

Output Arguments

q — Quotient

row or column vector

Quotient, returned as a row or column vector such that u = conv(v,q)+r.

Data Types: double | single

r — Remainder

row or column vector

Remainder, returned as a row or column vector such that u = conv(v,q)+r.

Data Types: double | single

Extended Capabilities

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).

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

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).

Distributed Arrays

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).

Version History

Introduced before R2006a