# A Quick Formulation of Compressed Sensing

2021-12-27 ☾ A very, very mininal derivation of the optimization

Compressed sensing is a signal processing method for reconstructing a signal from an undersampled set of measurements. Here, we will derive the optimization problem at the core of compressed sensing.

• Let $$x$$ be the true signal.

• Let $$x \stackrel{\mathcal U}{\mapsto} s$$ be the representation of $$x$$ in a functional basis space.

• Let $$x = \Psi s$$ for some universal transform basis $$\Psi$$ (e.g., Fourier, wavelet, etc.).

• Let $$y = Cx$$ be the measured signal, where $$C$$ is the measurement matrix.

Our objective is to obtain a subsampled version of $$s$$ from $$y$$, and then recover $$s \stackrel{\mathcal U^{-1}}{\mapsto} x$$, i.e.,

$$$\min_s \frac{1}{2} ||y - C\Psi s||_2^2 \,.$$$

As stated, this optimization is a underspecified / indeterminate problem (i.e., there are infinitely many solutions for $$s$$), so we constrain the solutions to be sparse:

$$$\min_s \frac{1}{2} ||y - C\Psi s||_2^2 + \lambda||s||_0 \,.$$$

However, minimizing $$l_0$$ norms is NP hard (because it requires combinatorial optimization), so we relax the problem with the $$l_1$$ norm:

$$$\min_s \frac{1}{2} ||y - C\Psi s||_2^2 + \lambda||s||_1 \,.$$$

This is a well-defined problem, and is solvable with linear programming (e.g., basis pursuit, denoising/matching). Additionally, Equation 3 is equivalent to Equation 2 under certain conditions (e.g., $$\nu$$-incoherence) and constraints on $$C\Psi$$. Then, given a noisy, downsampled measurement $$y$$, solve Equation 3 to get $$\hat s$$, and predict the original signal to be $$\hat x = \Psi \hat s$$ which is equivalent to $$\hat s \stackrel{\mathcal U^{-1}}{\mapsto} \hat x$$.