Add subgraph method to IncidencegraphInterface

[Robbybp]

Fixes

Reduces the number of times we need to walk expression trees (e.g. to compute AMPLRepns) when generating multiple different incidence graphs based on the same model.

Summary/Motivation:

Using IncidenceMethod.ampl_repn or IncidenceMethod.standard_repn seems to introduce significant overhead to incidence graph construction. Often, I want to work with several subgraphs based on a single model. For example, the subgraph that only includes equality constraints, or the subgraph on a particular set of variables (a particular block in a block triangularization, for instance). In these cases, constructing several incidence graphs becomes a performance bottleneck due to the effort required to determine variable-constraint incidence (edges in the graph). An IncidenceGraphInterface.subgraph method lets us get around this re-construction. Note that the incidence graph's data structures are copied, not modified in-place.

Changes proposed in this PR:

• Add IncidenceGraphInterface.subgraph method
• Allow construction of IncidenceGraphInterface from a tuple of (nz.Graph, list[Var], list[Constraint])
• Rewrite extract_bipartite_subgraph to use NetworkX's subgraph and relabel_nodes methods, instead of re-implementing my own versions of them.

Note that this builds on #3069

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[codecov]

Codecov Report

Attention: 1 lines in your changes are missing coverage. Please review.

Comparison is base (5903fbc) 88.32% compared to head (90c4078) 88.32%.

Files	Patch %	Lines
pyomo/contrib/incidence_analysis/interface.py	95.45%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #3122   +/-   ##
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  Coverage   88.32%   88.32%           
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  Files         832      832           
  Lines       92483    92492    +9     
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+ Hits        81685    81693    +8     
- Misses      10798    10799    +1     

Flag	Coverage Δ
linux	86.21% <95.45%> (+<0.01%)	⬆️
osx	75.67% <95.45%> (+<0.01%)	⬆️
other	86.41% <95.45%> (+<0.01%)	⬆️
win	83.63% <95.45%> (+<0.01%)	⬆️

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