Issue #17 Scaled gradient. moved this to an overridable function. Changed linesearch to modifySearchDirection

SimPEG/inverse/Optimize.py

@@ -18,7 +18,7 @@ class Minimize(object):
 
     maxIter = 20
     maxIterLS = 10
-    maxStep = np.inf    
+    maxStep = np.inf
     LSreduction = 1e-4
     LSshorten = 0.5
     tolF = 1e-1
@@ -56,14 +56,14 @@ class Minimize(object):
             self.printIter()
             if self.stoppingCriteria(): break
             p = self.findSearchDirection()
-            if self.maxStep < np.abs(p.max()):
-                p = self.maxStep*p/np.abs(p.max())
             pub.sendMessage('Minimize.searchDirection', minimize=self, p=p)
-            xt, passLS = self.linesearch(p) ## TODO: should be called modifyStep to be inclusive of trust region stuff etc.
-            pub.sendMessage('Minimize.linesearch', minimize=self, xt=xt)
+            p = self.scaleSearchDirection(p)
+            pub.sendMessage('Minimize.scaleSearchDirection', minimize=self, p=p)
+            xt, passLS = self.modifySearchDirection(p)
+            pub.sendMessage('Minimize.modifySearchDirection', minimize=self, xt=xt)
             if not passLS:
-                xt = self.linesearchBreak(p)
-                return self.xc
+                xt, caught = self.modifySearchDirectionBreak(p)
+                if not caught: return self.xc
             self.doEndIteration(xt)
             pub.sendMessage('Minimize.endIteration', minimize=self, xt=xt)
 
@@ -129,9 +129,6 @@ class Minimize(object):
         print "%d : iter      = %3d\t <= maxIter\t       = %3d"     % (self._STOP[4], self._iter, self.maxIter)
         print "%s DONE! %s\n" % ('='*25,'='*25)
 
-    def findSearchDirection(self):
-        return -self.g
-
     def stoppingCriteria(self):
         if self._iter == 0:
             self.fStop = self.f  # Save this for stopping criteria
@@ -147,7 +144,15 @@ class Minimize(object):
     def projection(self, p):
         return p
 
-    def linesearch(self, p):
+    def findSearchDirection(self):
+        return -self.g
+
+    def scaleSearchDirection(self, p):
+        if self.maxStep < np.abs(p.max()):
+            p = self.maxStep*p/np.abs(p.max())
+        return p
+
+    def modifySearchDirection(self, p):
         # Armijo linesearch
         descent = np.inner(self.g, p)
         t = 1
@@ -163,8 +168,24 @@ class Minimize(object):
         self._iterLS = iterLS
         return xt, iterLS < self.maxIterLS
 
-    def linesearchBreak(self, p):
+    def modifySearchDirectionBreak(self, p):
+        """
+            Code is called if modifySearchDirection fails
+            to find a descent direction.
+
+            The search direction is passed as input and
+            this function must pass back both a new searchDirection,
+            and if the searchDirection break has been caught.
+
+            By default, no additional work is done, and the
+            function returns a False indicating the break was not caught.
+
+            :param numpy.ndarray p: searchDirection
+            :rtype: numpy.ndarray,bool
+            :return: (xt, breakCaught)
+        """
         print 'The linesearch got broken. Boo.'
+        return p, False
 
     def doEndIteration(self, xt):
         # store old values
@@ -199,9 +220,7 @@ if __name__ == '__main__':
     checkDerivative(Rosenbrock, x0, plotIt=False)
 
     def listener1(minimize,p):
-        plt.plot(p)
-        plt.show()
-        print p
+        print 'hi:  ', p
     pub.subscribe(listener1, 'Minimize.searchDirection')
 
     xOpt = GaussNewton(maxIter=20,tolF=1e-10,tolX=1e-10,tolG=1e-10).minimize(Rosenbrock,x0)