  We present two parallel optimization algorithms for a convex function $f$.
The first algorithm optimizes over linear inequality constraints in a Hilbert
space, $\mathbb H$, and the second over a non convex polyhedron in $\mathbb
R^n$. The algorithms reduce the inequality constraints to equality constraints,
and garner information from subsets of constraints to speed up the process. Let
$r \in \mathbb N$ be the number of constraints and $\nu (\cdot)$ be the time
complexity of some process, then given enough threads, and information gathered
earlier from subsets of the given constraints, we compute an optimal point of a
polyhedral cone in $O(\nu (\langle \cdot,\cdot \rangle) + \nu (\min_A f)))$ for
affine space $A$, the intersection of the faces of the cone. We then apply the
method to all the faces of the polyhedron to find the linear inequality
constrained optimum. The methods works on constrained spaces with empty
interiors, furthermore no feasible point is required, and the algorithms
recognize when the feasible space is empty. The methods iterate over surfaces
of the polyhedron and the corresponding affine hulls using information garnered
from previous iterations of superspaces to speed up the process.
