apply denom max to independent denom

examples/run_all.py

@@ -198,6 +198,11 @@ def do_one_mmw(dirname, modname, runefstring, npyfile, mmwargstring):
        "--rel-gap 0.001 --max-iterations=50 "
        "--grad-rho --grad-order-stat 0.5 "
        "--default-rho=2 --solver-name={} --lagrangian --xhatshuffle".format(solver_name))
+do_one("farmer", "../../mpisppy/generic_cylinders.py", 3,
+       "--module-name farmer --num-scens 6 "
+       "--rel-gap 0.001 --max-iterations=50 "
+       "--grad-rho --indep-denom "
+       "--default-rho=2 --solver-name={} --lagrangian --xhatshuffle".format(solver_name))
 do_one("farmer", "../../mpisppy/generic_cylinders.py", 4,
        "--module-name farmer --num-scens 6 "
        "--rel-gap 0.001 --max-iterations=50 "

mpisppy/extensions/grad_rho.py

@@ -115,10 +115,16 @@ def _scen_indep_denom(self):
                 op=MPI.SUM,
             )
 
+        # max over *all* nodes
+        denom_max = max(max(denom) for denom in global_denoms.values())
         scen_indep_denom = {}
         for ndn, global_denom in global_denoms.items():
-            for i, v in enumerate(global_denom):
-                scen_indep_denom[ndn, i] = v
+            for i, denom in enumerate(global_denom):
+                # xbar is for rescaling, e.g., if the variable value is "naturally" small
+                if denom <= self.denom_bound * xbars[ndn, i]._value:
+                    scen_indep_denom[ndn, i] = max(denom_max, self.denom_bound * xbars[ndn, i]._value)
+                else:
+                    scen_indep_denom[ndn, i] = denom
 
         return scen_indep_denom
 

mpisppy/utils/config.py

@@ -984,9 +984,9 @@ def gradient_args(self):
                            default=False)
 
         self.add_to_config("grad_order_stat",
-                           description="order statistic for rho: must be between 0 (the min) and 1 (the max); 0.5 is the average",
+                           description="order statistic for rho: must be between 0 (the min) and 1 (the max); 0.5 is the average (default 0.5)",
                            domain=float,
-                           default=-1.0)
+                           default=0.5)
 
         self.add_to_config("grad_rho_relative_bound",
                            description="factor that bounds rho/cost",