Rename System.optimize to solve_constraints

This patch renames System.optimize to solve_constraints to reflect that the
method is not limited to symmetric problems, and that its sole purpose is the
establishment of Dirichlet constraints.

examples/adaptivity.py

@@ -69,10 +69,10 @@ def main(etype: str = 'square',
         ns.du = 'u - uexact'
 
         sqr = domain.boundary['corner'].integral('u^2 dS' @ ns, degree=degree*2)
-        cons = System(sqr, trial='u').optimize(droptol=1e-15)
+        cons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
 
         sqr = domain.boundary.integral('du^2 dS' @ ns, degree=7)
-        cons = System(sqr, trial='u').optimize(droptol=1e-15, constrain=cons)
+        cons = System(sqr, trial='u').solve_constraints(droptol=1e-15, constrain=cons)
 
         res = domain.integral('∇_k(v) ∇_k(u) dV' @ ns, degree=degree*2)
         args = System(res, trial='u', test='v').solve(constrain=cons)

examples/cylinderflow.py

@@ -129,7 +129,7 @@ def main(nelems: int = 99,
     ns.uwall_i = 'rotation ε_ij x_j' # clockwise positive rotation
 
     sqr = domain.boundary['inflow'].integral('Σ_i (u_i - uinf_i)^2 dS' @ ns, degree=degree*2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15) # constrain inflow boundary to unit horizontal flow
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15) # constrain inflow boundary to unit horizontal flow
 
     sqr = domain.integral('(.5 Σ_i (u_i - uinf_i)^2 - ∇_k(u_k) p) dV' @ ns, degree=degree*2)
     args = System(sqr, trial='u,p').solve(constrain=cons) # set initial condition to potential flow

examples/drivencavity.py

@@ -118,12 +118,12 @@ def main(nelems: int = 32,
 
     # strong enforcement of non-penetrating boundary conditions
     sqr = domain.boundary.integral('(u_k n_k)^2 dS' @ ns, degree=degree*2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
 
     if strongbc:
         # strong enforcement of tangential boundary conditions
         sqr = domain.boundary.integral('(ε_ij n_i (u_j - uwall_j))^2 dS' @ ns, degree=degree*2)
-        tcons = System(sqr, trial='u').optimize(droptol=1e-15)
+        tcons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
         cons['u'] = numpy.choose(numpy.isnan(cons['u']), [cons['u'], tcons['u']])
     else:
         # weak enforcement of tangential boundary conditions via Nitsche's method

examples/elasticity.py

@@ -51,7 +51,7 @@ def main(nelems: int = 24,
     ns.q_i = '-δ_i1'
 
     sqr = domain.boundary['top'].integral('u_k u_k dS' @ ns, degree=degree*2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
 
     # solve for equilibrium configuration
     energy = domain.integral('(E - u_i q_i) dV' @ ns, degree=degree*2)

examples/finitestrain.py

@@ -58,7 +58,7 @@ def main(nelems: int = 20,
 
     sqr = domain.boundary['left'].integral('u_k u_k dS' @ ns, degree=degree*2)
     sqr += domain.boundary['right'].integral('((u_0 - X_1 sin(2 angle) - cos(angle) + 1)^2 + (u_1 - X_1 (cos(2 angle) - 1) + sin(angle))^2) dS' @ ns, degree=degree*2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
 
     energy = domain.integral('energy dV' @ ns, degree=degree*2)
     args0 = System(energy, trial='u').solve(constrain=cons)

examples/laplace.py

@@ -72,7 +72,7 @@ def main(nelems: int = 10,
 
     sqr = domain.boundary['left'].integral('u^2 dS' @ ns, degree=degree*2)
     sqr += domain.boundary['top'].integral('(u - cosh(1) sin(x_0))^2 dS' @ ns, degree=degree*2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
 
     # The unconstrained entries of `u` are to be such that the residual
     # evaluates to zero for all possible values of `v`. The resulting array `u`

examples/platewithhole.py

@@ -137,10 +137,10 @@ def main(mode: Union[FCM, NURBS] = NURBS(),
     log.info('hole radius exact up to L2 error {:.2e}'.format(radiuserr))
 
     sqr = topo.boundary['sym'].integral('(u_i n_i)^2 dS' @ ns, degree=degree*2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15)
 
     sqr = topo.boundary['far'].integral('du_k du_k dS' @ ns, degree=20)
-    cons = System(sqr, trial='u').optimize(droptol=1e-15, constrain=cons)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-15, constrain=cons)
 
     res = topo.integral('∇_j(v_i) σ_ij dV' @ ns, degree=degree*2)
     args = System(res, trial='u', test='v').solve(constrain=cons)

examples/poisson.py

@@ -25,7 +25,7 @@ def main(nelems: int = 32):
     J = function.J(x)
 
     sqr = topo.boundary.integral(u**2 * J, degree=2)
-    cons = System(sqr, trial='u').optimize(droptol=1e-12)
+    cons = System(sqr, trial='u').solve_constraints(droptol=1e-12)
 
     energy = topo.integral((g @ g / 2 - u) * J, degree=1)
     args = System(energy, trial='u').solve(constrain=cons)

nutils/solver.py

@@ -404,8 +404,8 @@ def step(self, *, timestep: float, timetarget: str, historysuffix: str, argument
 
     @cache.function
     @log.withcontext
-    def optimize(self, *, droptol: Optional[float], arguments: Dict[str, numpy.ndarray] = {}, constrain: Dict[str, numpy.ndarray] = {}, linargs: Dict[str, Any] = {}) -> Tuple[Dict[str, numpy.ndarray], float]:
-        '''Optimize singular system.
+    def solve_constraints(self, *, droptol: Optional[float], arguments: Dict[str, numpy.ndarray] = {}, constrain: Dict[str, numpy.ndarray] = {}, linargs: Dict[str, Any] = {}) -> Tuple[Dict[str, numpy.ndarray], float]:
+        '''Solve singular system.
 
         This method is similar to ``solve``, but specific to symmetric linear
         systems, with the added ability to solve a limited class of singular
@@ -905,7 +905,7 @@ def optimize(target, functional: evaluable.asarray, *, tol: float = 0., argument
         raise TypeError('unexpected keyword arguments: {}'.format(', '.join(kwargs)))
     system = System(functional, *_split_trial_test(target))
     if droptol is not None:
-        return system.optimize(arguments=arguments, constrain=constrain or {}, linargs=linargs, droptol=droptol)
+        return system.solve_constraints(arguments=arguments, constrain=constrain or {}, linargs=linargs, droptol=droptol)
     method = None if system.is_linear else Newton() if linesearch is None else LinesearchNewton(strategy=linesearch, relax0=relax0, failrelax=failrelax)
     return system.solve(arguments=arguments, constrain=constrain or {}, linargs=linargs, method=method, tol=tol)