NLPy - gerad

Projected CGminimizex∈R n c T x + 1 2 xT Hx subject to Ax = bfrom nlpy.krylov.ppcg import ProjectedCGPCG = ProjectedCG(c, A=Jac, rhs=b, H=Hess)# or PCG = ProjectedCG(c, A=Jac, rhs=b, matvec=lambda p: H*p)PCG.Solve() # Solution is in PCG.xminimizex∈R n c T x + 1 2 xT Hx subject to Ax =0, ‖x‖ 2 ≤ ∆from nlpy.krylov.ppcg import ProjectedCGPCG = ProjectedCG(c, A=Jac, H=Hess, radius=10)PCG.Solve() # Solution is in PCG.x