Hi everyone, I have a two part question regarding the integration of the following optimization problem
min 1/2 u^T*K*u + u^T*f S.T. u >= 0 into SNES and TS 1) For SNES, assuming I am working with a linear FE equation, I have the following algorithm/steps for solving my problem a) Set an initial guess x b) Obtain residual r and jacobian A through functions SNESComputeFunction() and SNESComputeJacobian() respectively c) Form vector b = r - A*x d) Set Hessian equal to A, gradient to A*x, objective function value to 1/2*x^T*A*x + x^T*b, and variable (lower) bounds to a zero vector e) Call TaoSolve This works well at the moment, but my question is there a more "efficient" way of doing this? Because with my current setup, I am making a rather bold assumption that my problem would converge in one SNES iteration without the bounded constraints and does not have any unexpected nonlinearities. 2) How would I go about doing the above for time-stepping problems? At each time step, I want to solve a convex optimization subject to the lower bounds constraint. I plan on using backward euler and my resulting jacobian should still be compatible with the above optimization problem. Thanks, -- Justin Chang PhD Candidate, Civil Engineering - Computational Sciences University of Houston, Department of Civil and Environmental Engineering Houston, TX 77004 (512) 963-3262
