create branch

Author: nychiang

src/hiopbbpy/opt/boalgorithm.py

@@ -351,8 +351,17 @@ def minimizer_callback(self, x0s):
         xopt = y.x
         yopt = y.fun
       elif self.method == "trust-constr":
-        nonlinear_constraint = NonlinearConstraint(self.constraints['cons'], self.constraints['cl'], self.constraints['cu'], jac=self.constraints['jac'])
-        y = minimize(self.fun['obj'], x0, method=self.method, bounds=self.bounds, constraints=[nonlinear_constraint], options=self.solver_options)
+        constraints = []
+        if self.constraints:  # non-empty dict → constrained problem
+          nonlinear_constraint = NonlinearConstraint(
+              self.constraints['cons'],
+              self.constraints['cl'],
+              self.constraints['cu'],
+              jac=self.constraints.get('jac', None)
+          )
+          constraints.append(nonlinear_constraint)
+
+        y = minimize(self.fun['obj'], x0, method=self.method, bounds=self.bounds, constraints=constraints, options=self.solver_options)
         success = y.success
         if not success:
           msg = y.message

src/hiopbbpy/opt/optproblem.py

@@ -35,14 +35,21 @@ def __init__(self, objective, gradient, constraint:Union[Dict, List[Dict]], xbou
     self.eval_g  = gradient
     self.xl = [b[0] for b in xbounds]
     self.xu = [b[1] for b in xbounds]
-    self.cl = []
-    self.cu = []
     self.nvar = len(xbounds)
 
     self.ipopt_options = solver_options
     self.ipopt_options['sb'] = 'yes'
 
-    if isinstance(self.cons, list):
+    unconstrained = (
+            constraint is None
+            or constraint == {}
+            or constraint == []
+        )
+    if unconstrained:
+      self.cl = []
+      self.cu = []
+      self.ncon = 0
+    elif isinstance(self.cons, list):
       # constraints is provided as a list of dict, supported by SLSQP and Ipopt
       for con in self.cons:
         check_required_keys(con,['type', 'fun'])
@@ -60,7 +67,7 @@ def __init__(self, objective, gradient, constraint:Union[Dict, List[Dict]], xbou
       self.cl = constraint['cl']
       self.cu = constraint['cu']
     else:
-      raise ValueError("constraints must be provided as a dict of a list of dict.")
+      raise ValueError("constraints must be None, {}, [], a dict, or a list of dict.")
     self.ncon = len(self.cl)
 
     cyipopt = _require_cyipopt()
@@ -80,10 +87,13 @@ def gradient(self, x):
     return self.eval_g(x)
 
   def constraints(self, x):
+    if self.ncon == 0:
+      return np.zeros((0,), dtype=float)
+
     if isinstance(self.cons, list):
       return np.array([con['fun'](x) for con in self.cons])
     else:
-      return self.cons['cons'](x)
+      return np.asarray(self.cons['cons'](x), dtype=float)
 
   def jacobian(self, x):
     if isinstance(self.cons, list):

src/hiopbbpy/utils/util.py

@@ -120,16 +120,18 @@ def __init__(self, name='hiopbbpy'):
 
     # Create a logger instance with a given name        
     self._logger = logging.getLogger(name)
+    self._logger.propagate = False  # prevent double logging
 
     # Create a console output handler
-    ch = logging.StreamHandler()
+    if not self._logger.handlers:     
+      ch = logging.StreamHandler()
 
-    # Define the output format: logger name, and message
-    formatter = logging.Formatter('%(name)s %(message)s')
+      # Define the output format: logger name, and message
+      formatter = logging.Formatter('%(name)s %(message)s')
 
-    # Add the handle
-    ch.setFormatter(formatter)
-    self._logger.addHandler(ch)
+      # Add the handle
+      ch.setFormatter(formatter)
+      self._logger.addHandler(ch)
 
   def setlevel(self, level_str):
     level = getattr(logging, str(level_str).upper(), logging.INFO)