On Wed, Jun 15, 2011 at 09:19:32AM +0100, Garth N. Wells wrote: > > > On 14/06/11 20:53, Anders Logg wrote: > > On Tue, Jun 14, 2011 at 09:03:31PM +0200, Marie E. Rognes wrote: > >> On 06/14/2011 08:35 PM, Garth N. Wells wrote: > >>> > >>> > >>> On 14/06/11 19:24, Anders Logg wrote: > >>>> On Tue, Jun 14, 2011 at 10:19:20AM -0700, Johan Hake wrote: > >>>>> On Tuesday June 14 2011 03:33:59 Anders Logg wrote: > >>>>>> On Tue, Jun 14, 2011 at 09:25:17AM +0100, Garth N. Wells wrote: > >>>>>>> On 14/06/11 08:53, Anders Logg wrote: > >>>>>>>> 14 jun 2011 kl. 09:18 skrev "Garth N. Wells"<gn...@cam.ac.uk>: > >>>>>>>>> On 14/06/11 08:03, Marie E. Rognes wrote: > >>>>>>>>>> On 06/13/2011 11:16 PM, Anders Logg wrote: > >>>>>>>>>>>>> But while we are heading in that direction, how about > >>>>>>>>>>>>> abolishing the *Problem class(es) altogether, and just use > >>>>>>>>>>>>> LinearVariationalSolver and > >>>>>>>>>>>>> NonlinearVariationalSolver/NewtonSolver taking as input (a, > >>>>>>>>>>>>> L, > >>>>>>>>>>>> > >>>>>>>>>>>> bc) > >>>>>>>>>>>> > >>>>>>>>>>>>> and (F, dF, bcs), respectively. > >>>>>>>>>>> > >>>>>>>>>>> This will be in line with an old blueprint. We noted some time > >>>>>>>>>>> ago that problems/solvers are designed differently for linear > >>>>>>>>>>> systems Ax = b than for variational problems a(u, v) = L(v). > >>>>>>>>>>> For linear systems, we have solvers while for variational > >>>>>>>>>>> problems we have both problem and solver classes. > >>>>>>>>>>> > >>>>>>>>>>>>> I mean, the main difference lies in how to solve the > >>>>>>>>>>>>> problems, right? > >>>>>>>>>>> > >>>>>>>>>>> It looks like the only property a VariationalProblem has in > >>>>>>>>>>> addition to (forms, bc) + solver parameters is the parameter > >>>>>>>>>>> symmetric=true/false. > >>>>>>>>>>> > >>>>>>>>>>> If we go this route, we could mimic the design of the linear > >>>>>>>>>>> algebra solvers and provide two different options, one that > >>>>>>>>>>> offers more control, solver = KrylovSolver() + solver.solve(), > >>>>>>>>>>> and one quick option that just calls solve: > >>>>>>>>>>> > >>>>>>>>>>> 1. complex option > >>>>>>>>>>> > >>>>>>>>>>> solver = LinearVariationalSolver() # which arguments to > >>>>>>>>>>> constructor? solver.parameters["foo"] = ... u = solver.solve() > >>>>>>>>> > >>>>>>>>> I favour this option, but I think that the name > >>>>>>>>> 'LinearVariationalSolver' is misleading since it's not a > >>>>>>>>> 'variational solver', but solves variational problems, nor should > >>>>>>>>> it be confused with a LinearSolver that solves Ax = f. > >>>>>>>>> LinearVariationalProblem is a better name. For total control, we > >>>>>>>>> could have a LinearVariationalProblem constructor that accepts a > >>>>>>>>> GenericLinearSolver as an argument (as the NewtonSolver does). > >>>>>>>>> > >>>>>>>>>> For the eigensolvers, all arguments go in the call to solve. > >>>>>>>>>> > >>>>>>>>>>> 2. simple option > >>>>>>>>>>> > >>>>>>>>>>> u = solve(a, L, bc) > >>>>>>>>> > >>>>>>>>> I think that saving one line of code and making the code less > >>>>>>>>> explicit isn't worthwhile. I can foresee users trying to solve > >>>>>>>>> nonlinear problems with this. > >>>>>>>> > >>>>>>>> With the syntax suggested below it would be easy to check for errors. > >>>>>>>> > >>>>>>>>>> Just for linears? > >>>>>>>>>> > >>>>>>>>>>> 3. very tempting option (simple to implement in both C++ and > >>>>>>>>>>> Python) > >>>>>>>>>>> > >>>>>>>>>>> u = solve(a == L, bc) # linear u = solve(F == 0, J, bc) # > >>>>>>>>>>> nonlinear > >>>>>>>>> > >>>>>>>>> I don't like this on the same grounds that I don't like the > >>>>>>>>> present design. Also, I don't follow the above syntax > >>>>>>>> > >>>>>>>> I'm not surprised you don't like it. But don't understand why. It's > >>>>>>>> very clear which is linear and which is nonlinear. And it would be > >>>>>>>> easy to check for errors. And it would just be a thin layer on top of > >>>>>>>> the very explicit linear/nonlinear solver classes. And it would > >>>>>>>> follow the exact same design as for la with solver classes plus a > >>>>>>>> quick access solve function. > >>>>>>> > >>>>>>> Is not clear to me - possibly because, as I wrote above, I don't > >>>>>>> understand the syntax. What does the '==' mean? > >>>>>> > >>>>>> Here's how I see it: > >>>>>> > >>>>>> 1. Linear problems > >>>>>> > >>>>>> solve(a == L, bc) > >>>>>> > >>>>>> solve the linear variational problem a = L subject to bc > >>>>>> > >>>>>> 2. Nonlinear problems > >>>>>> > >>>>>> solve(F == 0, bc) > >>>>>> > >>>>>> solve the nonlinear variational problem F = 0 subject to bc > >>>>>> > >>>>>> It would be easy to in the first case check that the first operand (a) > >>>>>> is a bilinear form and the second (L) is a linear form. > >>>>>> > >>>>>> And it would be easy to check in the second case that the first > >>>>>> operand (F) is a linear form and the second is an integer that must be > >>>>>> zero. > >>>>>> > >>>>>> In both cases one can print an informative error message and catch any > >>>>>> pitfalls. > >>>>>> > >>>>>> The nonlinear case would in C++ accept an additional argument J for > >>>>>> the Jacobian (and in Python an optional additional argument): > >>>>>> > >>>>>> solve(F == 0, J, bc); > >>>>>> > >>>>>> The comparison operator == would for a == L return an object of class > >>>>>> LinearVariationalProblem and in the second case > >>>>>> NonlinearVariationalProblem. These two would just be simple classes > >>>>>> holding shared pointers to the forms. Then we can overload solve() to > >>>>>> take either of the two and pass the call on to either > >>>>>> LinearVariationalSolver or NonlinearVariationalSolver. > >>>>>> > >>>>>> I'm starting to think this would be an ideal solution. It's compact, > >>>>>> fairly intuitive, and it's possible to catch errors. > >>>>>> > >>>>>> The only problem I see is overloading operator== in Python if that > >>>>>> has implications for UFL that Martin objects to... :-) > >>>>> > >>>>> Wow, you really like magical syntaxes ;) > >>>> > >>>> Yes, a pretty syntax has been a priority for me ever since we > >>>> started. I think it is worth a lot. > >>>> > >>> > >>> Magic and pretty are not the same thing. > > > > That's true, but some magic is usually required to make pretty. > > > > Being able to write dot(grad(u), grad(v))*dx is also a bit magic. > > The step from there to solve(a == L) is short. > > > >>>>> The problem with this syntax is that who on earth would expect a > >>>>> VariationalProblem to be the result of an == operator... > >>>> > >>>> I don't think that's an issue. Figuring out how to solve variational > >>>> problems is not something one picks up by reading the Programmer's > >>>> Reference. It's something that will be stated on the first page of any > >>>> FEniCS tutorial or user manual. > >>>> > >>>> I think the solve(a == L) is the one missing piece to make the form > >>>> language complete. We have all the nice syntax for expressing forms in > >>>> a declarative way, but then it ends with > >>>> > >>>> problem = VariationalProblem(a, L) > >>>> problem.solve() > >>>> > >>>> which I think looks ugly. It's not as extreme as this example taken > >>> >from cppunit, but it follows the same "create object, call method on > >>>> object" paradigm which I think is ugly: > >>>> > >>>> TestResult result; > >>>> TestResultCollector collected_results; > >>>> result.addListener(&collected_results); > >>>> TestRunner runner; > >>>> runner.addTest(CppUnit::TestFactoryRegistry::getRegistry().makeTest()); > >>>> runner.run(result); > >>>> CompilerOutputter outputter(&collected_results, std::cerr); > >>>> outputter.write (); > >>>> > >>>>> I see the distinction between FEniCS developers who have programming > >>>>> versus > >>>>> math in mind when designing the api ;) > >>>> > >>>> It's always been one of the top priorities in our work on FEniCS to > >>>> build an API with the highest possible level of mathematical > >>>> expressiveness to the API. That sometimes leads to challenges, like > >>>> needing to develop a special form language, form compilers, JIT > >>>> compilation, the Expression class etc, but that's the sort of thing > >>>> we're pretty good at and one of the main selling points of FEniCS. > >>>> > >>> > >>> This is an exaggeration to me. The code > >>> > >>> problem = [Linear]VariationalProblem(a, L) > >>> u = problem.solve() > >>> > >>> is compact and explicit. It's a stretch to call it ugly. > > > > Yes, of course it's a stretch. It's not very ugly, but enough to > > bother me. > > > >>>>> Also __eq__ is already used in ufl.Form to compare two forms. > >>>> > >>>> I think it would be worth replacing the use of form0 == form1 by > >>>> repr(form0) == repr(form1) in UFL to be able to use __eq__ for this: > >>>> > >>>> class Equation: > >>>> def __init__(self, lhs, rhs): > >>>> self.lhs = lhs > >>>> self.rhs = rhs > >>>> > >>>> class Form: > >>>> > >>>> def __eq__(self, other): > >>>> return Equation(self, other) > >>>> > >>>> I understand there are other priorities, and others don't care as much > >>>> as I do about how fancy we can make the DOLFIN Python and C++ interface, > >>>> but I think this would make a nice final touch to the interface. > >>>> > >>> > >>> I don't see value in it. In fact the opposite - it introduces complexity > >>> and a degree of ambiguity. > > > > Complexity yes (but not much, it would require say around 50-100 > > additional lines of code that I will gladly contribute), but I don't > > think it's ambiguous. We could perform very rigorous and helpful > > checks on the input arguments. > > > >> Evidently, we all see things differently. I fully support Anders in > >> that mathematical expressiveness is one of the key features of > >> FEniCS, and I think that without pushing these types of boundaries > >> with regard to the language, it will end up as yet another finite > >> element library. > >> > >> Could we compromise on having the two versions, one explicit (based > >> on LinearVariational[Problem|Solver] or something of the kind) and > >> one terse (based on solve(x == y)) ? > > > > That's what I'm suggesting. The solve(x == y) would just rely on the > > more "explicit" version and do > > > > <lots of checks> > > LinearVariationalSolver solver(x, y, ...); > > solver.solve(u); > > > > So in essence what I'm asking for is please let me add that tiny layer > > on top of what we already have + remove the Problem classes (replaced > > by the Solver classes). > > > > It seems that we (almost?) all agree to add to the C++ side: > > LinearVariationalProblem > > NonlinearVariationalProblem
Not yet, I'm still thinking this through. Martin is making a good point that we need to differentiate between problems and solvers. I'll comment more later when I've thought this through. -- Anders > I think that 'FooVariationalProblem' is probably a better name than > 'FooVariationalSolver'. Could 'variational' (minimisation) and 'solver' > imply some abstract linear algebra problem? Most accurate is probably > 'FooVariationalProblemSolver', but it's a bit long. > > On the Python side, I don't mind others trying something fancy, but I'll > reserve my judgement ;). > > Garth > > > > > _______________________________________________ > Mailing list: https://launchpad.net/~dolfin > Post to : dolfin@lists.launchpad.net > Unsubscribe : https://launchpad.net/~dolfin > More help : https://help.launchpad.net/ListHelp _______________________________________________ Mailing list: https://launchpad.net/~dolfin Post to : dolfin@lists.launchpad.net Unsubscribe : https://launchpad.net/~dolfin More help : https://help.launchpad.net/ListHelp
