Remove references to scipopt.org; update DoE paper reference

.github/workflows/test_branches.yml

@@ -77,7 +77,8 @@ jobs:
         # Exclude:
         #  - Jenkins because it's behind a firewall
         #  - RTD because a magically-generated string triggers failures
-        exclude_urls: https://pyomo-jenkins.sandia.gov/,https://pyomo.readthedocs.io/en/%s/errors.html
+        #  - scipopt.org as they perform "validation" on the webclient (which fails)
+        exclude_urls: https://pyomo-jenkins.sandia.gov/,https://pyomo.readthedocs.io/en/%s/errors.html,https://www.scipopt.org
         # Exclude:
         #  - All gnu.org links because they consistently fail the checker
         #  - All lpsolve.sourceforge.net links because SF appears to reject from GHA

.github/workflows/test_pr_and_main.yml

@@ -89,7 +89,8 @@ jobs:
         # Exclude:
         #  - Jenkins because it's behind a firewall
         #  - RTD because a magically-generated string triggers failures
-        exclude_urls: https://pyomo-jenkins.sandia.gov/,https://pyomo.readthedocs.io/en/%s/errors.html
+        #  - scipopt.org as they perform "validation" on the webclient (which fails)
+        exclude_urls: https://pyomo-jenkins.sandia.gov/,https://pyomo.readthedocs.io/en/%s/errors.html,https://www.scipopt.org
         # Exclude:
         #  - All gnu.org links because they consistently fail the checker
         #  - All lpsolve.sourceforge.net links because SF appears to reject from GHA

.github/workflows/url_check.yml

@@ -40,7 +40,8 @@ jobs:
         # Exclude:
         #  - Jenkins because it's behind a firewall
         #  - RTD because a magically-generated string triggers failures
-        exclude_urls: https://pyomo-jenkins.sandia.gov/,https://pyomo.readthedocs.io/en/%s/errors.html
+        #  - scipopt.org as they perform "validation" on the webclient (which fails)
+        exclude_urls: https://pyomo-jenkins.sandia.gov/,https://pyomo.readthedocs.io/en/%s/errors.html,https://www.scipopt.org
         # Exclude:
         #  - All gnu.org links because they consistently fail the checker
         #  - All lpsolve.sourceforge.net links because SF appears to reject from GHA

doc/OnlineDocs/explanation/analysis/doe/doe.rst

@@ -15,11 +15,10 @@ Significant improvements and extensions were contributed by **Dan Laky**, and
 `Process Optimization & Modeling for Minerals Sustainability (PrOMMiS) <https://www.netl.doe.gov/prommis>`_ project
 and the `University of Notre Dame <https://www.nd.edu/>`_.
 
-If you use Pyomo.DoE, please cite:
-
-[Wang and Dowling, 2022] Wang, Jialu, and Alexander W. Dowling.
-"Pyomo.DOE: An open‐source package for model‐based design of experiments in Python."
-AIChE Journal 68.12 (2022): e17813. `https://doi.org/10.1002/aic.17813`
+If you use Pyomo.DoE, please cite the [PyomoDOE-paper]_:
+   Wang, Jialu, and Alexander W. Dowling. "Pyomo.DOE: An open‐source
+   package for model‐based design of experiments in Python", *AIChE Journal*,
+   68(12), e17813. 2022. DOI `10.1002/aic.17813 <https://doi.org/10.1002/aic.17813>`_
 
 Index of Pyomo.DoE documentation
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -32,4 +31,4 @@ Index of Pyomo.DoE documentation
    abstraction.rst
    objective.rst
    multexp.rst
-   uncertainty.rst
+   uncertainty.rst

doc/OnlineDocs/explanation/analysis/doe/guide.rst

@@ -16,7 +16,8 @@ MBDoE analysis. This is in line with the convention used in the parameter estima
 
 An example of the subclassed :class:`Experiment` object that builds and labels the model is shown in the next few sections.
 
-This guide illustrates the use of Pyomo.DoE using a reaction kinetics example ([WD22]_).
+This guide illustrates the use of Pyomo.DoE using a reaction kinetics
+example ([PyomoDOE-paper]_).
 
 .. math::
    :nowrap:

doc/OnlineDocs/explanation/analysis/doe/overview.rst

@@ -127,4 +127,5 @@ We can use the FIM or the covariance matrix to define the design criteria.
     A confidence ellipse is a geometric representation of the uncertainty in parameter
     estimates. It is derived from the covariance matrix :math:`\mathbf{V}`.
 
-In order to solve problems of the above, Pyomo.DoE implements the 2-stage stochastic program. Please see [WD22]_ for details.
+In order to solve problems of the above, Pyomo.DoE implements the
+2-stage stochastic program. Please see [PyomoDOE-paper]_ for details.

doc/OnlineDocs/explanation/solvers/mindtpy.rst

@@ -255,10 +255,7 @@ Augmented Penalty refers to the introduction of (non-negative) slack variables o
 Global Outer-Approximation
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Apart from the decomposition methods for convex MINLP problems [`Kronqvist et al., 2019`_], MindtPy provides an implementation of Global Outer Approximation (GOA) as described in [`Kesavan & Allgor, 2004`_], to provide optimality guaranteed for nonconvex MINLP problems. Here, the validity of the Mixed-integer Linear Programming relaxation of the original problem is guaranteed via the usage of Generalized McCormick envelopes, computed using the :ref:`interface to the MC++ package <MC++>`. The NLP subproblems, in this case, need to be solved to global optimality, which can be achieved through global NLP solvers such as `BARON`_ or `SCIP`_.
-
-.. _BARON: https://minlp.com/baron-solver
-.. _SCIP: https://www.scipopt.org/
+Apart from the decomposition methods for convex MINLP problems [`Kronqvist et al., 2019`_], MindtPy provides an implementation of Global Outer Approximation (GOA) as described in [`Kesavan & Allgor, 2004`_], to provide optimality guaranteed for nonconvex MINLP problems. Here, the validity of the Mixed-integer Linear Programming relaxation of the original problem is guaranteed via the usage of Generalized McCormick envelopes, computed using the :ref:`interface to the MC++ package <MC++>`. The NLP subproblems, in this case, need to be solved to global optimality, which can be achieved through global NLP solvers such as `BARON <https://minlp.com/baron-solver>`__ or `SCIP <https://www.scipopt.org>`__.
 
 
 Convergence

doc/OnlineDocs/explanation/solvers/pyros/getting_started.rst

@@ -14,14 +14,14 @@ Installation
 In advance of using PyROS to solve robust optimization problems,
 you will need (at least) one local nonlinear programming (NLP) solver
 (e.g.,
-`CONOPT <https://conopt.gams.com/>`_,
-`IPOPT <https://github.com/coin-or/Ipopt>`_,
-`Knitro <https://www.artelys.com/solvers/knitro/>`_)
+`CONOPT <https://conopt.gams.com/>`__,
+`IPOPT <https://github.com/coin-or/Ipopt>`__,
+`Knitro <https://www.artelys.com/solvers/knitro/>`__)
 and (at least) one global NLP solver
 (e.g.,
-`BARON <https://minlp.com/baron-solver>`_,
-`COUENNE <https://www.coin-or.org/Couenne/>`_,
-`SCIP <https://www.scipopt.org/>`_)
+`BARON <https://minlp.com/baron-solver>`__,
+`COUENNE <https://www.coin-or.org/Couenne/>`__,
+`SCIP <https://www.scipopt.org>`__)
 installed and licensed on your system.
 
 PyROS can be installed as follows:
@@ -57,10 +57,10 @@ PyROS can be installed as follows:
 
    Some tests involving deterministic NLP solvers may be skipped
    if
-   `IPOPT <https://github.com/coin-or/Ipopt>`_,
-   `BARON <https://minlp.com/baron-solver>`_,
+   `IPOPT <https://github.com/coin-or/Ipopt>`__,
+   `BARON <https://minlp.com/baron-solver>`__,
    or
-   `SCIP <https://www.scipopt.org/>`_
+   `SCIP <https://www.scipopt.org>`__
    is not 
    pre-installed and licensed on your system.
 

doc/OnlineDocs/getting_started/solvers.rst

@@ -79,13 +79,11 @@ the license requirements for their desired solver.
    * - SCIP (Command-line)
      - N/A
      - ``conda install ‑c conda‑forge scip``
-     - `License <https://www.scipopt.org/scip/doc/html/LICENSE.php>`__
-       `Docs <https://www.scipopt.org/index.php#download>`__
+     - (see `SCIP <https://www.scipopt.org>`__ project)
    * - SCIP (Python)
      - ``pip install pyscipopt``
      - ``conda install ‑c conda‑forge pyscipopt``
-     - `License <https://www.scipopt.org/scip/doc/html/LICENSE.php>`__
-       `Docs <https://www.scipopt.org/index.php#download>`__
+     - (see `SCIP <https://www.scipopt.org>`__ project)
    * - XPRESS
      - ``pip install xpress``
      - ``conda install ‑c fico‑xpress xpress``

doc/OnlineDocs/reference/bibliography.rst

@@ -39,6 +39,10 @@ These publications describe various Pyomo capabilities or subpackages:
    and algebraic equations", *Mathematical Programming Computation*, 10(2),
    187-223. 2018.
 
+.. [PyomoDOE-paper] Wang, Jialu, and Alexander W. Dowling. "Pyomo.DOE: An open‐source
+   package for model‐based design of experiments in Python", *AIChE Journal*,
+   68(12), e17813. 2022. DOI `10.1002/aic.17813 <https://doi.org/10.1002/aic.17813>`_
+
 .. [Parmest-paper] Katherine A. Klise, Bethany L. Nicholson, Andrea
    Staid, David L.Woodruff.  "Parmest: Parameter Estimation Via Pyomo."
    *Computer Aided Chemical Engineering*, 47, 41-46. 2019.
@@ -179,7 +183,3 @@ Bibliography
 
 .. [NW99] Nocedal, Jorge, and Stephen J. Wright, eds. Numerical
    optimization. New York, NY: Springer New York, 1999.
-
-.. [WD22] Wang, Jialu, and Alexander W. Dowling. "Pyomo.DOE: An open‐source
-   package for model‐based design of experiments in Python", *AIChE Journal*,
-   68(12), e17813. 2022. DOI `10.1002/aic.17813Y <https://doi.org/10.1002/aic.17813Y>`_