Add Logic-Based Discrete-Steepest Descent Algorithm in GDPOpt

[ZedongPeng]

Summary/Motivation:

This PR introduces the implementation of the Logic-Based Discrete Steepest Descent algorithm in GDPOpt.

The Logic-based Discrete-Steepest Descent Algorithm (LD-SDA) is a solution method for GDP problems involving ordered Boolean variables. The LD-SDA reformulates these ordered Boolean variables into integer decisions called external variables. The LD-SDA solves the reformulated GDP problem using a two-level decomposition approach where the upper-level subproblem determines external variable configurations. Subsequently, the remaining continuous and discrete variables are solved as a subproblem only involving those constraints relevant to the given external variable arrangement, effectively taking advantage of the structure of the GDP problem.

More details in the paper https://arxiv.org/abs/2405.05358 .

@emma58 @bernalde

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[codecov]

Codecov Report

Attention: Patch coverage is 26.90355% with 144 lines in your changes missing coverage. Please review.

Project coverage is 88.37%. Comparing base (1ada528) to head (1144dc1).
Report is 746 commits behind head on main.

Files	Patch %	Lines
pyomo/contrib/gdpopt/ldsda.py	23.40%	144 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #3331      +/-   ##
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- Coverage   88.50%   88.37%   -0.13%     
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  Files         868      869       +1     
  Lines       98418    98614     +196     
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+ Hits        87100    87154      +54     
- Misses      11318    11460     +142     

Flag	Coverage Δ
linux	85.90% <26.90%> (-0.13%)	⬇️
osx	75.19% <26.90%> (-0.10%)	⬇️
other	86.40% <26.90%> (-0.12%)	⬇️
win	83.71% <26.90%> (-0.12%)	⬇️

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[ZedongPeng]

I will add tests to increase the code coverage.

[ZedongPeng]

Hi @dovallev and @David-Linan,

This PR includes a general implementation of LDSDA. When you have some time, could you please review it? Thanks in advance!

[blnicho]

@ZedongPeng is this ready for review now?