  Goldreich suggested candidates of one-way functions and pseudorandom
generators included in $\mathsf{NC}^0$. It is known that randomly generated
Goldreich's generator using $(r-1)$-wise independent predicates with $n$ input
variables and $m=C n^{r/2}$ output variables is not pseudorandom generator with
high probability for sufficiently large constant $C$. Most of the previous
works assume that the alphabet is binary and use techniques available only for
the binary alphabet. In this paper, we deal with non-binary generalization of
Goldreich's generator and derives the tight threshold for linear programming
relaxation attack using local marginal polytope for randomly generated
Goldreich's generators. We assume that $u(n)\in \omega(1)\cap o(n)$ input
variables are known. In that case, we show that when $r\ge 3$, there is an
exact threshold
$\mu_\mathrm{c}(k,r):=\binom{k}{r}^{-1}\frac{(r-2)^{r-2}}{r(r-1)^{r-1}}$ such
that for $m=\mu\frac{n^{r-1}}{u(n)^{r-2}}$, the LP relaxation can determine
linearly many input variables of Goldreich's generator if
$\mu>\mu_\mathrm{c}(k,r)$, and that the LP relaxation cannot determine
$\frac1{r-2} u(n)$ input variables of Goldreich's generator if
$\mu<\mu_\mathrm{c}(k,r)$. This paper uses characterization of LP solutions by
combinatorial structures called stopping sets on a bipartite graph, which is
related to a simple algorithm called peeling algorithm.
