Least-Squares Solvers API Update

[shanemcq18]

Changes to the opinf.lstsq API and improvements to the documentation.

• opinf.model classes now receive solvers in the constructor, not in fit(). This change will be useful for future models that require specific solvers. Updated ROM class and tutorials accordingly.
• New SolverTemplate class and inheritance guide for creating new solvers.
• Renamed attributes to match OpInf terminology.
  • A --> data_matrix, called $D$ in the docs.
  • B --> lhs_matrix, called $Z$ in the docs. Warning: fit() receives $Z$, not $Z^T$!
  • $X$ is replaced with $\hat{O}$. Warning: predict() returns $\hat{O}$, not $\hat{O}^T$!
• Renamed two Tikhonov solver classes:
  • L2SolverDecoupled --> L2DecoupledSolver
  • TikhonovSolverDecoupled --> TikhonovDecoupledSolver
• Tikhonov solvers no longer have a default regularization value of zero.

Before:

>>> model = opinf.models.ContinuousModel("A")
>>> solver = opinf.lstsq.L2Solver(regularizer=1e-2)
>>> model.fit(states, ddts, inputs, solver=solver)

After:

>>> solver = opinf.lstsq.L2Solver(regularizer=1e-2)
>>> model = opinf.models.ContinuousModel("A", solver=solver)
>>> model.fit(states, ddts, inputs)