# basisPursuit

Recover sparse signal using the basis pursuit algorithm

## Syntax

``[Xr,MSE,lambda] = basisPursuit(A,Y)``
``[Xr,MSE,lambda] = basisPursuit(___,Name=Value)``

## Description

example

````[Xr,MSE,lambda] = basisPursuit(A,Y)` recovers the sparse signal approximation `Xr` of `Y` by solving the Basis Pursuit Denoising Problem using the `sensingDictionary` `A`. The `basisPursuit` function also returns the minimum mean squared error `MSE` and the corresponding Lagrangian parameter `lambda`.```
````[Xr,MSE,lambda] = basisPursuit(___,Name=Value)` specifies options using one or more name-value arguments in addition to the input argument in the previous syntax. For example, ```[Xr,MSE,lambda] = basisPursuit(A,Y,RelTol=5e-2)``` sets a relative tolerance of `5e-2`.```

## Examples

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`load wecg`

Create a sensing dictionary that can be applied to the signal. Use the `dct` basis type.

`D = sensingDictionary(Size=length(wecg),Type={'dct'});`

Obtain the best fit for the signal using the dictionary and basis pursuit. Obtain the minimum mean squared error.

```[XBP,MSE,lambda] = basisPursuit(D,wecg); MSE```
```MSE = 1.2349e-04 ```

Extract the sensing dictionary matrix. Use the matrix to construct the approximation.

```A = subdict(D,1:D.Size(1),1:D.Size(2)); wecgR = A*XBP;```

Obtain the norm of the difference between the original signal and its approximation.

`norm(wecg-wecgR)`
```ans = 0.5029 ```

Plot the signal and the approximation. Plot the difference at the same scale.

```subplot(2,1,1) plot(wecg) hold on plot(wecgR) hold off legend("Original","Approximation") title("Original Signal and Approximation") ylimits = get(gca,"YLim"); subplot(2,1,2) plot(wecg-wecgR) ylim(ylimits) title("Difference Between Original Signal and Approximation")``` ## Input Arguments

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Sensing dictionary, specified as a `sensingDictionary` object.

Sensor measurements, specified as a vector `Y` such that ```Y = AX```, where `X` is a sparse signal.

Data Types: `single` | `double`
Complex Number Support: Yes

### Name-Value Arguments

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`, where `Name` is the argument name and `Value` is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: `Xr = basisPursuit(A,Y,RelTol=1e-3)` recovers `Xr` using stopping criteria based on a relative tolerance of `1e-3`.

Maximum number of iterations executed to recover the sparse signal, specified as a positive integer.

Example: `Xr = basisPursuit(A,Y,maxIterations=35)` recovers `Xr` using at most 35 iterations.

Data Types: `single` | `double`

Relative tolerance used to recover the signal, specified as a positive scalar. The stopping criteria is based on the relative tolerance.

Example: `Xr = basisPursuit(A,Y,RelTol=1e-3)` recovers `Xr` using stopping criteria based on a relative tolerance of `1e-3`.

Data Types: `single` | `double`

Absolute tolerance used to recover the signal, specified as a positive scalar. The stopping criteria is based on the absolute tolerance.

Example: `Xr = basisPursuit(A,Y,AbsTol=1e-4)` recovers `Xr` using stopping criteria based on a absolute tolerance of `1e-4`.

Data Types: `single` | `double`

Maximum error used to recover the signal, specified as a positive scalar. The `basisPursuit` function recovers the `Xr` that satisfies

`$‖Y-A{X}_{r}{‖}_{2}^{2}\text{\hspace{0.17em}}\le \text{\hspace{0.17em}}\text{MaxErr}\text{.}$`

If unspecified, `Xr` is the solution of the Basis Pursuit Denoising Problem.

Example: `Xr = basisPursuit(A,Y,MaxErr=1e-1)` recovers `Xr` using stopping criteria based on a maximum error of `1e-1`.

Data Types: `single` | `double`

## Output Arguments

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Sparse signal recovered, returned as a vector.

Data Types: `single` | `double`
Complex Number Support: Yes

Minimum mean squared error, returned as a scalar.

Data Types: `single` | `double`

Lagrangian parameter, returned as a scalar.

Data Types: `single` | `double`

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### Basis Pursuit Denoising Problem

Basis pursuit denoising recovers the sparse signal `Xr` by solving

`$\underset{X}{\mathrm{min}}\frac{1}{2}‖Y-AX{‖}_{2}^{2}+\lambda ‖X{‖}_{1},$`

where

• `A` — Sensing dictionary

• `Y` — Measurement vector

• λ — Lagrangian parameter. Adjusting λ controls the balance between sparsity and accuracy of reconstruction.

## Version History

Introduced in R2022a