Do you have a repo with your source or an example? In my experience conjugate gradient, powell's method or any other linear optimization is essentially useless with >100 variables or so. Optimizing anything with thousands of variables is a feat, I'd be interested to see more details of how you do it.
Everything I do in this field is commercial-in-confidence, so I can't share any of my existing code like this. I may yet write this up, get clearance, and blog about it, but that won't happen real soon.
I also found Powell's method largely useless in high dimensional spaces, but I think that's because it's semi-static. The search point doesn't gain momentum, so it quickly gets stuck in local minima. If the search point "picks up speed" then it can coast up and back out of minima. Then you bleed off the speed at greater or lesser rates so the search location (which you can think of as a particle) eventually doesn't have enough energy to get out of the minimum it's in. This is very similar to Boltzmann-like behaviour - where you end up depends less on the area (measure) of your catchment, and more on the depth of your local optimum. In this sense it shares some characteristics with SA.
Hence the "ballistic" part of this.
And truth be told, in several hundred dimensions everything is a crap shoot. High-dimensional spheres are spikey, and the terrain you're exploring is effectively a mesa with wells and flagpoles. I've just had more success with SSBHD than with SA, GA, Powell, gradient-descent, or anything else.
