Improve welcome to IDAES background material (#146)

* Added links and references to background material

* Forgot to save changes

* Apply suggestions from code review

Co-authored-by: Miranda Mundt <55767766+mrmundt@users.noreply.github.com>

---------

Co-authored-by: Keith Beattie <ksbeattie@lbl.gov>
Co-authored-by: Bethany Nicholson <blnicho@users.noreply.github.com>
Co-authored-by: Miranda Mundt <55767766+mrmundt@users.noreply.github.com>

Author: 4 people

idaes_examples/notebooks/index.md

@@ -25,11 +25,15 @@ The online examples have been created with the
 
 ### Prerequisites
 
-Examples in this documentation are rigorously *tested* to ensure that they work with the *latest* version of the IDAES 
+**Install the latest version of IDAES.** Examples in this documentation are rigorously *tested* to ensure that they work with the *latest* version of the IDAES 
 software. 
 For more information on installing IDAES on your platform,
 please refer to the [IDAES documentation](https://idaes-pse.readthedocs.io/en/latest/index.html).
 
+**Learn about mathematical optimization and Pyomo.** IDAES is a state-of-the-art equation-oriented modeling and optimization environment. Below are recommended topics and references:
+* *Mathematical optimization* especially nonlinear programs (optimization problems) and chemical engineering applications. {cite}`Postek2025`, along with the [companion website](https://mobook.github.io/MO-book/intro.html) and [overview video](https://www.youtube.com/watch?v=DPv-7TeSTNs), are the best resources for a user new to mathematical optimization. {cite}`biegler1997systematic`, {cite}`biegler2010nonlinear`, and {cite}`grossmann2021advanced` are excellent references for advanced users. [Professor Alexander Dowling's course website](https://ndcbe.github.io/optimization/intro.html) includes Jupyter notebooks and Pyomo examples inspired by these texts.
+* *Pyomo*. IDAES is built upon Pyomo, which is an open-source algebraic modeling environment. New users will likely find {cite}`Postek2025` along with its [companion website](https://mobook.github.io/MO-book/intro.html) and the [ND Pyomo Cookbook](https://ndcbe.github.io/ND-Pyomo-Cookbook/README.html), as the easiest introduction to Pyomo. Other excellent resources include {cite}`bynum2021pyomo` and the official [Pyomo documentation](https://pyomo.readthedocs.io/en/stable/).
+
 ### Getting the source code
 The full source code for these examples is available from the 
 [IDAES examples repository](https://github.com/IDAES/examples) on GitHub.

idaes_examples/notebooks/references.bib

@@ -14,4 +14,45 @@ @incollection{miller2018idaes
     Bethany Nicholson and Carl Laird and Lorenz T. Biegler and Debangsu Bhattacharyya and Nikolaos V. Sahinidis and
     Ignacio E. Grossmann and Chrysanthos E. Gounaris and Dan Gunter},
     keywords = {Multiscale modeling, conceptual design, process optimization, dynamic optimization, parameter estimation}
+}
+
+@book{Postek2025,
+    title = {Hands-On Mathematical Optimization with Python},
+    author = { Krzysztof Postek and Alessandro Zocca and Vrije Universiteit Amsterdam and Joaquim A. S. Gromicho},
+    publisher = {Cambridge University Press},
+    year = {2025},
+    isbn = {9781009493505},
+    url = {https://www.cambridge.org/us/universitypress/subjects/mathematics/optimization-or-and-risk-analysis/hands-mathematical-optimization-python}
+}
+
+@book{biegler1997systematic,
+  title={Systematic methods of chemical process design},
+  author={Biegler, Lorenz T and Grossmann, Ignacio E and Westerberg, Arthur W},
+  year={1997},
+  publisher={Prentice Hall},
+  isbn={9780134924229}
+}
+
+@book{biegler2010nonlinear,
+  title={Nonlinear programming: concepts, algorithms, and applications to chemical processes},
+  author={Biegler, Lorenz T},
+  year={2010},
+  publisher={SIAM}
+}
+
+@book{grossmann2021advanced,
+  title={Advanced optimization for process systems engineering},
+  author={Grossmann, Ignacio E},
+  year={2021},
+  publisher={Cambridge University Press},
+  isbn = {9781108831659}
+}
+
+@book{bynum2021pyomo,
+  title={Pyomo-optimization modeling in python},
+  author={Bynum, Michael L and Hackebeil, Gabriel A and Hart, William E and Laird, Carl D and Nicholson, Bethany L and Siirola, John D and Watson, Jean-Paul and Woodruff, David L and others},
+  volume={67},
+  number={s 32},
+  year={2021},
+  publisher={Springer}
 }