Barry, That's good to know thanks.
On a related note, is it possible for VI to one day include linear equality constraints? Thanks, Justin On Mon, Aug 31, 2015 at 7:13 PM, Barry Smith <[email protected]> wrote: > > > On Aug 31, 2015, at 7:36 PM, Justin Chang <[email protected]> wrote: > > > > Coming back to this, > > > > Say I now want to ensure the DMP for advection-diffusion equations. The > linear operator is now asymmetric and non-self-adjoint (assuming I do > something like SUPG or finite volume), meaning I cannot simply solve this > problem without any manipulation (e.g. normalizing the equations) using > TAO's optimization solvers. Does this statement also hold true for SNESVI? > > SNESVI doesn't care about symmetry etc > > > > > Thanks, > > Justin > > > > On Fri, Apr 3, 2015 at 7:38 PM, Barry Smith <[email protected]> wrote: > > > > > On Apr 3, 2015, at 7:35 PM, Justin Chang <[email protected]> wrote: > > > > > > I guess I will have to write my own code then :) > > > > > > I am not all that familiar with Variational Inequalities at the > moment, but if my Jacobian is symmetric and positive definite and I only > have lower and upper bounds, doesn't the problem simply reduce to that of a > convex optimization? That is, with SNES act as if it were Tao? > > > > Yes, I think that is essentially correctly. > > > > Barry > > > > > > > > On Fri, Apr 3, 2015 at 6:35 PM, Barry Smith <[email protected]> > wrote: > > > > > > Justin, > > > > > > We haven't done anything with TS to handle variational > inequalities. So you can either write your own backward Euler (outside of > TS) that solves each time-step problem either as 1) an optimization problem > using Tao or 2) as a variational inequality using SNES. > > > > > > More adventurously you could look at the TSTHETA code in TS (which > is a general form that includes Euler, Backward Euler and Crank-Nicolson > and see if you can add the constraints to the SNES problem that is solved; > in theory this is straightforward but it would require understanding the > current code (which Jed, of course, overwrote :-). I think you should do > this. > > > > > > Barry > > > > > > > > > > On Apr 3, 2015, at 12:31 PM, Justin Chang <[email protected]> wrote: > > > > > > > > I am solving the following anisotropic transient diffusion equation > subject to 0 bounds: > > > > > > > > du/dt = div[D*grad[u]] + f > > > > > > > > Where the dispersion tensor D(x) is symmetric and positive definite. > This formulation violates the discrete maximum principles so one of the > ways to ensure nonnegative concentrations is to employ convex optimization. > I am following the procedures in Nakshatrala and Valocchi (2009) JCP and > Nagarajan and Nakshatrala (2011) IJNMF. > > > > > > > > The Variational Inequality method works gives what I want for my > transient case, but what if I want to implement the Tao methodology in TS? > That is, what TS functions do I need to set up steps a) through e) for each > time step (also the Jacobian remains the same for all time steps so I would > only call this once). Normally I would just call TSSolve() and let the > libraries and functions do everything, but I would like to incorporate > TaoSolve into every time step. > > > > > > > > Thanks, > > > > > > > > -- > > > > Justin Chang > > > > PhD Candidate, Civil Engineering - Computational Sciences > > > > University of Houston, Department of Civil and Environmental > Engineering > > > > Houston, TX 77004 > > > > (512) 963-3262 > > > > > > > > On Thu, Apr 2, 2015 at 6:53 PM, Barry Smith <[email protected]> > wrote: > > > > > > > > An alternative approach is for you to solve it as a (non)linear > variational inequality. See src/snes/examples/tutorials/ex9.c > > > > > > > > How you should proceed depends on your long term goal. What > problem do you really want to solve? Is it really a linear time dependent > problem with 0 bounds on U? Can the problem always be represented as an > optimization problem easily? What are and what will be the properties of > K? For example if K is positive definite then likely the bounds will remain > try without explicitly providing the constraints. > > > > > > > > Barry > > > > > > > > > On Apr 2, 2015, at 6:39 PM, Justin Chang <[email protected]> wrote: > > > > > > > > > > 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 > > > > > > > > > > > > > > > > > > > > -- > > > > Justin Chang > > > > PhD Candidate, Civil Engineering - Computational Sciences > > > > University of Houston, Department of Civil and Environmental > Engineering > > > > Houston, TX 77004 > > > > (512) 963-3262 > > > > > > > > > > > > > > > -- > > > Justin Chang > > > PhD Candidate, Civil Engineering - Computational Sciences > > > University of Houston, Department of Civil and Environmental > Engineering > > > Houston, TX 77004 > > > (512) 963-3262 > > > > > >
