# Minimizing $L_2$ norm with constrained residual sum of squares (RSS)

I have some complex-valued time-series data, $$y \in \mathbb{C}^n$$ - a signal with additive Gaussian white noise. The goal is to find the Fourier coefficients of this signal.

Ideally, you would just do a discrete Fourier transform, but in this case the data is non-uniformly sampled and every data point has some arbitrary complex amplitude (a "feature" of the measurement method) multiplied with it (prior to injection of noise). So, I want to solve for the underlying signal model via a linear model.

We have $$y = A x + \epsilon$$ where $$A \in \mathbb{C}^{n \times p}$$ is essentially the analogue of the DFT Matrix but also includes the complex amplitudes, $$x \in \mathbb{C}^p$$ are the Fourier coefficients we want to solve for, and $$\epsilon \sim \mathcal{CN}(0, \sigma^2)$$ is a complex normal noise term.

Due to non-uniform sampling, $$A$$ does not necessarily have full rank and is most definitely ill-conditioned (large ratio between highest and lowest singular values). So, we need to add regularization.

So, the problem I am trying to solve is:

$$\min_x \|x\|^2_2 \,\, \text{ subject to } \|y - Ax\|^2_2 \le \delta \tag{1}\label{eq1}$$

This is a nice way to frame the problem for two reasons:

1. Our knowledge of the noise term allows us to set $$\delta \approx n \sigma^2$$ - this is the expected RSS. This constraint prevents underfitting.

2. $$\|x\|^2_2$$ has the interpretation of being the "total power" in the model. Finding the model with minimum power prevents overfitting.

These conditions should hit the right spot in the bias-variance tradeoff.

How do you go about solving this sort of problem?

Here's some of my work on the problem:

I don't know how to rigorously prove this statement, but I am fairly certain my problem is essentially equivalent to Ridge Regression.

$$\hat{x}_\lambda = \underset{x}{\operatorname{argmin}} \, (\| y - A x \|^2_2 + \lambda \|x\|^2_2)$$

Is this true?

You can claim that as $$\lambda$$ decreases, $$\delta$$ increases (if you switch the inequality to an equality in (1)). Ideally, there's a nice way to numerically solve (1) preserving the interpretability of $$\delta$$.

An idea is to write the RSS as a function of $$\lambda$$,

$$f(\lambda) = \| y - A \hat{x}_\lambda \|^2_2$$

and solve for $$\lambda$$ in the equation: $$f(\lambda) = \delta$$.

This gives us:

$$\| y - A \hat{x}_\lambda \|^2_2 = \delta \\ (y - A \hat{x}_\lambda)^* (y - A \hat{x}_\lambda) = \delta$$

where $$X^*$$ is the conjugate transpose of $$X$$. We know that,

$$\hat{x}_\lambda = (A^* A + \lambda I)^{-1} A^* y$$

So, we have:

$$(y - A (A^* A + \lambda I)^{-1} A^* y)^* \, (y - A (A^* A + \lambda I)^{-1} A^* y) = \delta$$

I am not sure how to approach this.

Let's consider the least squares regression problem:

$$\begin{bmatrix} y &=& Ax &+& \epsilon \\ 0 &=& \lambda I_p x &+& e\end{bmatrix}$$

where we have "stacked" the two regression problems $$y = Ax + \epsilon$$ and $$0 = \lambda I_p x + e$$. This is equivalent to ridge regression: see @whuber's answer to How to derive the ridge regression solution?.

Clearly, if we set $$\lambda = 0$$, we have an unconstrained regression problem $$y = Ax + \epsilon$$, and, as $$\lambda \to \infty$$, the problem approaches the unconstrained regression problem $$0 = I_p x + e$$. The latter problem is evidently equivalent to $$\min_x ||x||_2^2$$.

Let $$x_{\lambda}$$ be the least squares solution to this problem for a given value of $$\lambda$$. Then we define:

$$||y - Ax_{\lambda}||_2^2 = \delta_{\lambda}$$

$$x_{\lambda}$$ is the solution to $$\min_x ||x||_2^2 \; \text{s.t.} \; ||y - Ax||_2^2 \leq\delta_{\lambda}$$.

One proof of this is by contradiction. Assume there is an $$x'$$ that achieves a smaller value of $$||x||_2^2$$ given the constraint. Then it would have achieved a smaller value of $$||e||_2^2 \; (= \lambda^2||x||_2^2)$$ in the original regression problem while achieving the same or smaller value of $$||\epsilon||_2^2$$. Since the least squares solution minimizes $$||e||_2^2 + ||\epsilon||_2^2$$, and $$x'$$ achieves a smaller value of one or both terms while not increasing the other, $$x_{\lambda}$$ cannot be the least squares solution. But it is by definition, therefore, no such $$x'$$ can exist.

How do we find $$\lambda$$ such that $$\delta_{\lambda} =$$ the desired $$\delta$$? A little thought will convince us that $$\delta_{\lambda}$$ is strictly monotonic (increasing) in $$\lambda$$, implying that this is a straightforward one-dimensional root finding problem, and can be solved using any number of off-the-shelf routines.

Here's some sample code in R, solving a simpler (non-complex) version of your problem:

library(MASS)

# Original problem
A <- matrix(rnorm(100), 10, 10)
x_true <- rep(1, 10)
y <- (A %*% x_true + rnorm(10))[,1]

# Extension to Ridge formulation
yr <- c(y, rep(0, 10))

# Objective function for root-finding routine
foo <- function(lambda, delta) {
Ar <- rbind(A, diag(lambda, 10))
e <- residuals(lm(yr~Ar))[1:10]
sum(e*e) - delta
}

# We handwave over bracketing lambda, but, due to monotonicity, it's easy to do
lambda <- uniroot(foo, lower=0.0001, upper=100, delta=5)$root Ar <- rbind(A, diag(lambda, 10)) m1 <- lm(yr~Ar) # Compare this to our target delta of 5 e <- residuals(m1)[1:10] sum(e*e)  4.999904 > coef(m1) # constrained coefficients (Intercept) Ar1 Ar2 Ar3 Ar4 Ar5 Ar6 -0.9502549 0.5227828 1.1742053 0.7032605 0.5329104 0.7776386 0.5640878 Ar7 Ar8 Ar9 Ar10 0.6192965 1.2160958 0.8605507 0.3305862  The unconstrained coefficients would, naturally, all equal $$0$$. • Why do say$\delta$is monotonically increasing in$\lambda$? I thought it would be decreasing. – XYZT Sep 28, 2021 at 22:03 • The way I have it set up is that large$\lambda$will force$x$closer to$0$, which will naturally increase the errors associated with$y - Ax$. This happens because the second block of the regression will cause least squares to try to make$\lambda I_p x$as close to zero as possible, so large$\lambda \to $small$x\$. Sep 29, 2021 at 1:01