Least squares and (non-)linearity of parameters

I have a question about least squares and about what happens, if the function that we minimize, $E(P)$, is not linear in its parameters $P$.

Assume we want to minimize a function (the exact terms are not so important, these are just examples) $$E(\mathbf{p}) = = \frac{1}{2}\| E_a(\mathbf{p}) + \alpha E_b(\mathbf{p}) \|^2$$ where $$E_a(\mathbf{p}) = \sum_i\| x_i-p_i\|^2$$ and $$E_b(\mathbf{p}) = \sum_i\| y_i-p_i\|^2$$ where $\mathbf{p}\in\mathbb{R}^N$, $x_i$ and $y_i$ are scalars, and $p_i$ refers to the $i$-th element of $\mathbf{p}$.

Now, on the way to setting up a (hopefully linear) equation system, computing the partial derivative of $E(\mathbf{p})$ wrt. $p_m$ gets us $$\frac{\partial E(\mathbf{p})}{\partial p_m} = \| E_a(\mathbf{p}) + \alpha E_b(\mathbf{p}) \| \Bigl( \frac{\partial E_a(\mathbf{p})}{\partial p_m} + \alpha\frac{\partial E_b(\mathbf{p})}{\partial p_m} \Bigr)$$

My questions: Is $\frac{\partial E(\mathbf{p})}{\partial p_m}$ really linear in $\mathbf{p}$ (despite of the additional $\|\cdot\|^2$)?

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This isn't least squares, since $E_a(p)$ and $E_b(p)$ are both quadratic in $p$, hence $E(p)$ is quartic in $p$. You might call it least hypercubes... – Chris Taylor Feb 16 '12 at 16:17
Alright, that's what I thought ... However, the paper, where I read about this, says that they express this exact problem as a linear system of equations but give not additional details. Do you have an idea what they might have done? Exist there any approximation tricks that I might not be aware of? – nils Feb 16 '12 at 16:22
It would help if you could link to the paper. – Chris Taylor Feb 16 '12 at 16:31
Sure, the paper can be found here. The minimization problem is in eq. (8). The statement, that it is expressed as a linear equation system is on the following page at the beginning of the 3rd paragraph. – nils Feb 16 '12 at 16:41
A quick thought: since $E_a(p)$ and $E_b(p)$ are convex, their sum is a convex function as well, right? Doesn't that mean, that the minimum would remain the same, even if $\|\cdot\|^2$ in $E(p)$ were dropped? – nils Feb 17 '12 at 8:47

As Chris explained in his comments, this problem is not linear in the parameters $\mathbf{p}$.
However, the two functions $E_a(\mathbf{p})$ and $E_b(\mathbf{p})$ are convex. Luckily, according to Wikipedia, the sum of two convex functions is also a convex function. That means, if $\|\cdot\|^2$ is dropped from $E(\mathbf{p})$ not only does the minimum remain identical, but the resulting equation system is linear in its parameters again - my problem solved!