# Gradient descent efficiently learns positive definite deep linear residual networks

### Abstract

We analyze algorithms
that aim to approximate
a function
$f(x) = \Phi x$
mapping $\Re^d$ to $\Re^d$ using deep linear
neural networks, i.e.\ a function $h$ parameterized
by matrices $\Theta_1,...,\Theta_L$ defined by
$h(x) = \Theta_L \Theta_{L-1} ... \Theta_1 x$. We focus
on algorithms that learn through gradient descent on the population
quadratic loss in the case that the distribution over the inputs is
isotropic. We provide polynomial bounds on the number of
iterations for gradient descent to approximate the
optimum, in the case where
the initial hypothesis $\Theta_1 = ... = \Theta_L = I$
has loss bounded by a small enough
constant. On the other hand,
we show that gradient descent fails to converge for
$\Phi$ whose distance from the identity
is a larger constant, and we show that some forms
of regularization toward the identity in each layer do
not help.
If $\Phi$ is symmetric positive definite,
we show that an algorithm that initializes $\Theta_i = I$
learns an $\epsilon$-approximation of $f$
using a number of updates polynomial in $L$,
the condition number of $\Phi$, and $\log(d/\epsilon)$. In contrast, we show
that if the $\Phi$ is symmetric and has a
negative eigenvalue, that all members of a class of algorithms
that perform gradient descent with identity initialization,
and optionally regularize toward the identity in each layer, fail to
converge. We analyze an algorithm for
the case that $\Phi$ satisfies $u^{\top} \Phi u > 0$ for all
$u$, but may not
be symmetric; this algorithm uses two regularizers
that maintain the invariants that
$u^{\top} \Theta_L \Theta_{L-1} ... \Theta_1 u > 0$ for all $u$,
and that ``balance'' $\Theta_1 ... \Theta_L$ so that they
have the same singular values.