Changeset 2760

Timestamp:

01/04/10 16:55:12 (15 years ago)

Author:

Mathieu Morlighem

Message:

normalize only if last gradient was bad (ie: search_scalar=0)

File:

• issm/trunk/src/c/parallel/control_core.cpp (modified) (2 diffs)

issm/trunk/src/c/parallel/control_core.cpp

@@ -113 +113 @@
                 gradjcompute_results->FindResult(&grad_g,"grad_g");
                 delete gradjcompute_results;
-                _printf_("%s\n","      done.");
-
-                _printf_("%s\n","      normalizing directions...");
-                Orthx(&new_grad_g,grad_g,grad_g_old);
+
+                /*Normalize if last gradient not satisfying (search_scalar==0)*/
+                if (n>0 && search_scalar==0){
+                        _printf_("%s","      normalizing directions...");
+                        Orthx(&new_grad_g,grad_g,grad_g_old);
+                        _printf_("%s\n"," done.");
+                }
+                else{ 
+                        VecDuplicate(grad_g,&new_grad_g);
+                        VecCopy(grad_g,new_grad_g);
+                }
                 VecFree(&grad_g); VecFree(&grad_g_old); 
                 grad_g_old=new_grad_g;
                 VecToMPISerial(&grad_g_double,new_grad_g);
-                _printf_("%s\n","      done.");
-
-                _printf_("%s\n","      optimizing along gradient direction...");
+
+                _printf_("%s\n","      optimizing along gradient direction");
                 optargs.model=model;
                 optargs.param_g=param_g; optargs.grad_g=grad_g_double; optargs.inputs=inputs;optargs.n=n;
                 optpars.xmin=0; optpars.xmax=1; optpars.tolerance=tolx; optpars.maxiter=(int)maxiter[n];optpars.cm_jump=cm_jump[n];
                 BrentSearch(&search_scalar,J+n,&optpars,&objectivefunctionC,&optargs);
-                _printf_("%s\n","      done.");
-
-                _printf_("%s\n","      updating parameter using optimized search scalar...");
+
+                _printf_("%s","      updating parameter using optimized search scalar...");
                 for(i=0;i<numberofnodes;i++)param_g[i]=param_g[i]+search_scalar*optscal[n]*grad_g_double[i];
-                _printf_("%s\n","      done.");
-
-                _printf_("%s\n","      constraining the new distribution...");    
+                _printf_("%s\n"," done.");
+
+                _printf_("%s","      constraining the new distribution...");    
                 ControlConstrainx(param_g,numberofnodes,cm_min,cm_max,control_type);
-                _printf_("%s\n","      done.");
+                _printf_("%s\n"," done.");
                 
                 _printf_("%s%i%s%g\n","      value of misfit J after optimization #",n+1,": ",J[n]);
@@ -167 +172 @@
                 //some temporary saving
                 if (((n+1)%5)==0){
-                        _printf_("%s\n","      saving temporary results...");
+                        _printf_("%s","      saving temporary results...");
                         ControlRestart(model,param_g);
-                        _printf_("%s\n","      done.");
+                        _printf_("%s\n"," done.");
                 }
         }
 
         /*Write results to disk: */
-        _printf_("%s\n","      preparing final velocity solution...");
+        _printf_("%s","      preparing final velocity solution");
         /*Launch diagnostic with the last parameter distribution*/
         if (control_steady){