Merge branch 'main' into add-greybox

Author: djlaky

.github/workflows/test_branches.yml

@@ -44,9 +44,10 @@ jobs:
         python-version: '3.10'
     - name: Black Formatting Check
       run: |
-        # Note v24.4.1 fails due to a bug in the parser
+        # Note v24.4.1 fails due to a bug in the parser. Project-level
+        # configuration is inherited from pyproject.toml.
         pip install 'black!=24.4.1'
-        black . -S -C --check --diff --exclude examples/pyomobook/python-ch/BadIndent.py
+        black . --check --diff
     - name: Spell Check
       uses: crate-ci/typos@master
       with: 

.github/workflows/test_pr_and_main.yml

@@ -55,9 +55,10 @@ jobs:
         python-version: '3.10'
     - name: Black Formatting Check
       run: |
-        # Note v24.4.1 fails due to a bug in the parser
+        # Note v24.4.1 fails due to a bug in the parser. Project-level
+        # configuration is inherited from pyproject.toml.
         pip install 'black!=24.4.1'
-        black . -S -C --check --diff --exclude examples/pyomobook/python-ch/BadIndent.py
+        black . --check --diff
     - name: Spell Check
       uses: crate-ci/typos@master
       with: 

doc/OnlineDocs/contribution_guide.rst

@@ -37,13 +37,17 @@ run:
 
     # Auto-apply correct formatting
    pip install black
-   black -S -C <path> --exclude examples/pyomobook/python-ch/BadIndent.py
+   black <path>
    # Find typos in files
    conda install typos
    typos --config .github/workflows/typos.toml <path>
    
-If the spell-checker returns a failure for a word that is spelled correctly,
-please add the word to the ``.github/workflows/typos.toml`` file.
+If the spell-checker returns a failure for a word that is spelled
+correctly, please add the word to the ``.github/workflows/typos.toml``
+file. Note also that ``black`` reads from ``pyproject.toml`` to
+determine correct configuration, so if you are running ``black``
+indirectly (for example, using an IDE integration), please ensure you
+are not overriding the project-level configuration set in that file.
 
 Online Pyomo documentation is generated using `Sphinx <https://www.sphinx-doc.org/en/master/>`_
 with the ``napoleon`` extension enabled. For API documentation we use of one of these 

doc/OnlineDocs/explanation/modeling/math_programming/sets.rst

@@ -29,15 +29,30 @@ and postpones creation of its members:
 
 The :class:`Set` class takes optional arguments such as:
 
-- ``dimen`` = Dimension of the members of the set
-- ``doc`` = String describing the set
-- ``filter`` = A Boolean function used during construction to indicate if a
-  potential new member should be assigned to the set
-- ``initialize`` = An iterable containing the initial members of the Set, or
-  function that returns an iterable of the initial members the set.
-- ``ordered`` = A Boolean indicator that the set is ordered; the default is ``True``
-- ``validate`` = A Boolean function that validates new member data
-- ``within`` = Set used for validation; it is a super-set of the set being declared.
+``dimen``
+    Dimension of the members of the set; ``None`` for "jagged" sets
+    (where members do not have a uniform length).
+
+``doc``
+    String describing the set
+
+``filter``
+    A Boolean function used during construction to indicate if a
+    potential new member should be assigned to the set
+
+``initialize``
+    An iterable containing the initial members of the Set, or
+    function that returns an iterable of the initial members the set.
+
+``ordered``
+    A Boolean indicator that the set is ordered; the default is ``True``
+    (Set is ordered by insertion order)
+
+``validate``
+    A Boolean function that validates new member data
+
+``within``
+    Set used for validation; it is a super-set of the set being declared.
 
 In general, Pyomo attempts to infer the "dimensionality" of Set
 components (that is, the number of apparent indices) when they are
@@ -451,8 +466,40 @@ for this model, a toy data file (in AMPL "``.dat``" format) would be:
 
    >>> inst = model.create_instance('src/scripting/Isinglecomm.dat')
 
-This can also be done somewhat more efficiently, and perhaps more clearly,
-using a :class:`BuildAction` (for more information, see :ref:`BuildAction`):
+A similar result can be accomplished more efficiently (because we only
+iterate over the Arcs twice) using initialization functions that accept
+only a model block and return a ``dict`` with all the information needed
+for the indexed set:
+
+.. doctest::
+   :hide:
+
+   >>> model = inst
+   >>> del model.NodesIn
+   >>> del model.NodesOut
+
+.. testcode::
+
+    def NodesIn_init(m):
+        # Create a dict to show NodesIn list for every node
+        d = {i: [] for i in m.Nodes}
+        # loop over the arcs and record the end points
+        for i, j in model.Arcs:
+            d[j].append(i)
+        return d
+    model.NodesIn = pyo.Set(model.Nodes, initialize=NodesIn_init)
+
+    def NodesOut_init(m):
+        d = {i: [] for i in m.Nodes}
+        for i, j in model.Arcs:
+            d[i].append(j)
+        return d
+    model.NodesOut = pyo.Set(model.Nodes, initialize=NodesOut_init)
+
+Alternatively, this can also be done even more efficiently, and perhaps
+more clearly, outside the context of Set initialization.  For concrete
+models, scripts can explicitly add elements to the Sets after
+declaration:
 
 .. doctest::
    :hide:
@@ -463,8 +510,29 @@ using a :class:`BuildAction` (for more information, see :ref:`BuildAction`):
 
 .. testcode::
 
+   model.NodesIn = pyo.Set(model.Nodes, within=model.Nodes)
    model.NodesOut = pyo.Set(model.Nodes, within=model.Nodes)
+
+   # loop over the arcs and record the end points
+   for i, j in model.Arcs:
+       model.NodesIn[j].add(i)
+       model.NodesOut[i].add(j)
+
+For abstract models, that action must be deferred to instance
+construction time using a :class:`BuildAction` (for more information,
+see :ref:`BuildAction`):
+
+.. doctest::
+   :hide:
+
+   >>> model = inst
+   >>> del model.NodesIn
+   >>> del model.NodesOut
+
+.. testcode::
+
    model.NodesIn = pyo.Set(model.Nodes, within=model.Nodes)
+   model.NodesOut = pyo.Set(model.Nodes, within=model.Nodes)
 
    def Populate_In_and_Out(model):
        # loop over the arcs and record the end points

doc/OnlineDocs/explanation/solvers/pyros.rst

@@ -906,13 +906,13 @@ Observe that the log contains the following information:
   information, (UTC) time at which the solver was invoked,
   and, if available, information on the local Git branch and
   commit hash.
-* **Summary of solver options** (lines 19--38).
-* **Preprocessing information** (lines 39--41).
+* **Summary of solver options** (lines 19--40).
+* **Preprocessing information** (lines 41--43).
   Wall time required for preprocessing
   the deterministic model and associated components,
   i.e., standardizing model components and adding the decision rule
   variables and equations.
-* **Model component statistics** (lines 42--58).
+* **Model component statistics** (lines 44--61).
   Breakdown of model component statistics.
   Includes components added by PyROS, such as the decision rule variables
   and equations.
@@ -927,7 +927,7 @@ Observe that the log contains the following information:
   The number of truly uncertain parameters detected during preprocessing
   is also noted in parentheses
   (in which "eff." is an abbreviation for "effective").
-* **Iteration log table** (lines 59--69).
+* **Iteration log table** (lines 62--69).
   Summary information on the problem iterates and subproblem outcomes.
   The constituent columns are defined in detail in
   :ref:`the table following the snippet <table-iteration-log-columns>`.
@@ -953,7 +953,7 @@ Observe that the log contains the following information:
 
 * **Termination statistics** (lines 89--94). Summary of statistics related to the
   iterate at which PyROS terminates.
-* **Exit message** (lines 95--96).
+* **Exit message** (lines 95--97).
 
 
 .. _solver-log-snippet:
@@ -963,10 +963,10 @@ Observe that the log contains the following information:
    :linenos:
 
    ==============================================================================
-   PyROS: The Pyomo Robust Optimization Solver, v1.3.8.
+   PyROS: The Pyomo Robust Optimization Solver, v1.3.9.
           Pyomo version: 6.9.3dev0
           Commit hash: unknown
-          Invoked at UTC 2025-05-05T00:00:00.000000+00:00
+          Invoked at UTC 2025-07-21T00:00:00.000000+00:00
 
    Developed by: Natalie M. Isenberg (1), Jason A. F. Sherman (1),
                  John D. Siirola (2), Chrysanthos E. Gounaris (1)
@@ -977,7 +977,7 @@ Observe that the log contains the following information:
    of Energy's Institute for the Design of Advanced Energy Systems (IDAES).
    ==============================================================================
    ================================= DISCLAIMER =================================
-   PyROS is still under development. 
+   PyROS is still under development.
    Please provide feedback and/or report any issues by creating a ticket at
    https://github.com/Pyomo/pyomo/issues/new/choose
    ==============================================================================
@@ -998,6 +998,7 @@ Observe that the log contains the following information:
     backup_local_solvers=[]
     backup_global_solvers=[]
     subproblem_file_directory=None
+    subproblem_format_options={'bar': {'symbolic_solver_labels': True}}
     bypass_local_separation=False
     bypass_global_separation=False
     p_robustness={}
@@ -1025,33 +1026,34 @@ Observe that the log contains the following information:
    ------------------------------------------------------------------------------
    Itn  Objective    1-Stg Shift  2-Stg Shift  #CViol  Max Viol     Wall Time (s)
    ------------------------------------------------------------------------------
-   0     3.5838e+07  -            -            5       1.8832e+04   0.759
-   1     3.5838e+07  2.9329e-09   5.0030e-10   5       2.1295e+04   1.573
-   2     3.6285e+07  7.6526e-01   2.0398e-01   2       2.2457e+02   2.272
-   3     3.6285e+07  7.7212e-13   1.2525e-10   0       7.2940e-08g  5.280
+   0     3.5838e+07  -            -            5       1.8832e+04   0.611
+   1     3.5838e+07  1.2289e-09   1.5886e-12   5       2.8919e+02   1.702
+   2     3.6269e+07  3.1647e-01   1.0432e-01   4       2.9020e+02   3.407
+   3     3.6285e+07  7.6526e-01   1.4596e-04   7       7.5966e+03   5.919
+   4     3.6285e+07  1.1608e-11   2.2270e-01   0       1.5084e-12g  8.823
    ------------------------------------------------------------------------------
    Robust optimal solution identified.
    ------------------------------------------------------------------------------
    Timing breakdown:
-   
+
    Identifier                ncalls   cumtime   percall      %
    -----------------------------------------------------------
-   main                           1     5.281     5.281  100.0
+   main                           1     8.824     8.824  100.0
         ------------------------------------------------------
-        dr_polishing              3     0.155     0.052    2.9
-        global_separation        27     1.280     0.047   24.2
-        local_separation        108     2.200     0.020   41.7
-        master                    4     0.727     0.182   13.8
-        master_feasibility        3     0.103     0.034    1.9
-        preprocessing             1     0.021     0.021    0.4
-        other                   n/a     0.794       n/a   15.0
+        dr_polishing              4     0.547     0.137    6.2
+        global_separation        27     0.978     0.036   11.1
+        local_separation        135     4.645     0.034   52.6
+        master                    5     1.720     0.344   19.5
+        master_feasibility        4     0.239     0.060    2.7
+        preprocessing             1     0.013     0.013    0.2
+        other                   n/a     0.681       n/a    7.7
         ======================================================
    ===========================================================
-   
+
    ------------------------------------------------------------------------------
    Termination stats:
-    Iterations            : 4
-    Solve time (wall s)   : 5.281
+    Iterations            : 5
+    Solve time (wall s)   : 8.824
     Final objective value : 3.6285e+07
     Termination condition : pyrosTerminationCondition.robust_optimal
    ------------------------------------------------------------------------------

examples/doc/samples/case_studies/diet/DietProblem.tex

@@ -62,7 +62,7 @@ \subsection*{Build the model}
 
 At this point we must start defining the rules associated with our parameters and variables.  We begin with the most important rule, the cost rule, which will tell the model to try and minimize the overall cost.  Logically, the total cost is going to be the sum of how much is spent on each food, and that value in turn is going to be determined by the cost of the food and how much of it is purchased.  For example, if three \$5 hamburgers and two \$1 apples are purchased, than the total cost would be $3 \cdot 5 + 2 \cdot 1 = 17$.  Note that this process is the same as taking the dot product of the amounts vector and the costs vector.
 
-To input this, we must define the cost rule, which we creatively call costRule as 
+To input this, we must define the cost rule, which we creatively call costRule as
 
 \begin{verbatim}def costRule(model):
     return sum(model.costs[n]*model.amount[n] for n in model.foods)
@@ -75,10 +75,10 @@ \subsection*{Build the model}
 
 This line defines the objective of the model as the costRule,  which Pyomo interprets as the value it needs to minimize; in this case it will minimize our costs.  Also, as a note, we defined the objective as ``model.cost'' which is not to be confused with the parameter we defined earlier as ``model.costs,'' despite their similar names.  These are two different values and accidentally giving them the same name will cause problems when trying to solve the problem.
 
-We must also create a rule for the volume consumed.  The construction of this rule is similar to the cost rule as once again we take the dot product, this time between the volume and amount vectors. 
+We must also create a rule for the volume consumed.  The construction of this rule is similar to the cost rule as once again we take the dot product, this time between the volume and amount vectors.
 
 \begin{verbatim}def volumeRule(model):
-    return sum(model.volumes[n]*model.amount[n] for n in 
+    return sum(model.volumes[n]*model.amount[n] for n in
         model.foods) <= model.max_volume
 
 model.volume = pyo.Constraint(rule=volumeRule)
@@ -90,7 +90,7 @@ \subsection*{Build the model}
 
 \begin{verbatim}
 def nutrientRule(n, model):
-    value = sum(model.nutrient_value[n,f]*model.amount[f] 
+    value = sum(model.nutrient_value[n,f]*model.amount[f]
         for f in model.foods)
     return (model.min_nutrient[n], value, model.max_nutrient[n])
 
@@ -160,7 +160,7 @@ \subsection*{Data entry}
 
 The amount of spaces between each element is irrelevant (as long as there is at least one) so the matrix should be formatted for ease of reading.
 
-Now that we have finished both the model and the data file save them both. It's convention to give the model file a .py extension and the data file a .dat extension.  
+Now that we have finished both the model and the data file save them both. It's convention to give the model file a .py extension and the data file a .dat extension.
 
 \subsection*{Solution}