conv - Convolution and polynomial multiplication - MATLAB (original) (raw)

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Convolution and polynomial multiplication

Syntax

Description

w = conv([u,v](#bucr8kb-1-uv)) returns the convolution of vectors u and v. If u and v are vectors of polynomial coefficients, convolving them is equivalent to multiplying the two polynomials.

example

w = conv([u,v](#bucr8kb-1-uv),[shape](#bucr8kb-1-shape)) returns a subsection of the convolution, as specified by shape. For example, conv(u,v,'same') returns only the central part of the convolution, the same size as u, and conv(u,v,'valid') returns only the part of the convolution computed without the zero-padded edges.

example

Examples

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Polynomial Multiplication via Convolution

Create vectors u and v containing the coefficients of the polynomials x2+1 and 2x+7.

Use convolution to multiply the polynomials.

w contains the polynomial coefficients for 2x3+7x2+2x+7.

Vector Convolution

Create two vectors and convolve them.

u = [1 1 1]; v = [1 1 0 0 0 1 1]; w = conv(u,v)

w = 1×9

 1     2     2     1     0     1     2     2     1

The length of w is length(u)+length(v)-1, which in this example is 9.

Central Part of Convolution

Create two vectors. Find the central part of the convolution of u and v that is the same size as u.

u = [-1 2 3 -2 0 1 2]; v = [2 4 -1 1]; w = conv(u,v,'same')

w has a length of 7. The full convolution would be of length length(u)+length(v)-1, which in this example would be 10.

Input Arguments

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u,v — Input vectors

vectors

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

When u or v are of type single, then the output is of type single. Otherwise, conv converts inputs to type double and returns type double.

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical
Complex Number Support: Yes

shape — Subsection of convolution

'full' (default) | 'same' | 'valid'

Subsection of the convolution, specified as 'full', 'same', or 'valid'.

More About

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Convolution

The convolution of two vectors, u and v, represents the area of overlap under the points as v slides across u. Algebraically, convolution is the same operation as multiplying polynomials whose coefficients are the elements of u and v.

Let m = length(u) and n = length(v). Thenw is the vector of length m+n-1 whosekth element is

The sum is over all the values of j that lead to legal subscripts for u(j) and v(k-j+1), specifically j = max(1,k+1-n):1:min(k,m). When m = n, this gives

w(1) = u(1)*v(1) w(2) = u(1)*v(2)+u(2)*v(1) w(3) = u(1)*v(3)+u(2)*v(2)+u(3)*v(1) ... w(n) = u(1)*v(n)+u(2)*v(n-1)+ ... +u(n)v(1) ... w(2n-1) = u(n)*v(n)

Using this definition, conv calculates the direct convolution of two vectors, rather than the FFT-based convolution.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Theconv function supports tall arrays with the following usage notes and limitations:

• The inputs u and v must be column vectors.
• If shape is 'full' (default), then only one ofu or v can be a tall array.
• If shape is 'same' or 'valid', then v cannot be a tall array.

For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

For information about C/C++ code generation limitations, see Variable-Sizing Restrictions for Code Generation of Toolbox Functions (MATLAB Coder).

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU 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™.

The conv function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). 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