In line serach the initial alpha can be restricted to avoid overflow …

…in the forward model if one has this information. It saves un-neccessary alpha back tracking in particular in the initial step in BFGS.

escript/py_src/costfunctions.py

@@ -81,7 +81,6 @@ def getStatistics(self):
         out="Number of cost function evaluations: %d\n" % self.Value_calls
         out+="Number of gradient evaluations: %d\n" % self.Gradient_calls
         out+="Number of inverse Hessian evaluations: %d\n" % self.InverseHessianApproximation_calls
-        out+="Number of gradient evaluations: %d\n" % self.Gradient_calls
         out+="Number of inner product evaluations: %d\n" % self.DualProduct_calls
         out+="Number of argument evaluations: %d\n" % self.Arguments_calls
         out+="Number of norm evaluations: %d" % self.Norm_calls
@@ -264,6 +263,23 @@ def getGradient(self, m, *args):
         """
         raise NotImplementedError
 
+    def getSqueezeFactor(self, m, p, *args):
+        """
+        The new solution is calculated as m+a*p with a>0. This function allows to provide an upper bound
+        for a to make sure that m+a*p is valid typically to avoid overflow when the cost function is evaluated.
+        the solver will take action to make sure that the value of a is not too small.
+
+        :param m: a solution approximation
+        :type m: m-type
+        :param p: an increment to the solution
+        :type m: m-type
+        :param args: pre-calculated values for ``m`` from `getArgumentsAndCount()`
+        :rtype: positive ``float`` or None
+
+        :note: Overwrite this method to implement a cost function.
+        """
+        return None
+
     def getInverseHessianApproximation(self, r, m, *args, initializeHessian = True):
         """
         returns an approximate evaluation *p* of the inverse of the Hessian

escript/py_src/minimizer.py

@@ -992,8 +992,15 @@ def run(self, m):
                 else:
                     alphaMin = self._relAlphaMin
                 self.getLineSearch().setOptions(alphaMin=alphaMin)
-                alpha = max(alpha, alphaMin*1.10)
-                self.logger.debug("Starting line search with alphaMin, alpha  = %g, %g" % (alphaMin, alpha))
+                alpha = max(alpha, alphaMin * 1.10)
+                alpha_s = self.getCostFunction().getSqueezeFactor(m, p)
+                if not alpha_s is None:
+                    assert alpha_s >0
+                    self.logger.debug("Safe alpha value given as %g." % (alpha_s,))
+                    alpha_s*=0.5
+                    if alpha_s > alphaMin:
+                        alpha = min(alpha, alpha_s)
+                self.logger.info("Starting line search with alpha  = %g."% (alpha, ))
                 try:
                     phi_new = self.getLineSearch().run(phi, alpha)
                     alpha = phi_new.alpha