Algorithm choice in numerically solving large constrained nonlinear optimization

Hi all,

I am wondering about algorithm choice in solving numerically a large constrained nonlinear optimization problem.

The problem has ~1 million variables and ~500,000 nonlinear constraints. You are allowed infinite processors (for parallel computing), and a sample solution is known to start the optimization. You can also assume that the first and second derivatives of the constraint functions are known.

The problem can be stated:

Minimize *f(***x**) over **x**, subject to the constraints *h(***x**)=0, *g(***x**) <= 0, and **lb**<**x**<**ub**

This means that **x** is a vector of variables limited with lower and upper bounds, and subject to some number of constraints.

I have explored many types of algorithms, but do not have a conclusive answer for which is the best for this type of problem (large-scale, known derivatives, etc). Any ideas for a good, parallelizable algorithm?