MP-Opt-Model 4.2

What's New in MP-Opt-Model 4.2

Released May 10, 2024

Below is a summary of the changes since version 4.1 of MP-Opt-Model. See
the CHANGES.md file for all the gory details. For release notes
for previous versions, see Appendix C of the MP-Opt-Model User's
Manual.

New Features:

• Option for opt_model.add_lin_constraint() to provide/store the
  transpose of the A matrix instead of the original. This can
  potentially save significant memory for sparse matrices with many
  more columns than rows. E.g. storage constraints in MOST
  for 8760 hour planning horizon.
• Add support to nlps_master() for calling nlps_<my_solver>() by
  setting opt.alg to '<MY_SOLVER>' to allow for handling custom
  NLP solvers.
• Add support to miqps_master() for calling miqps_<my_solver>()
  by setting opt.alg to '<MY_SOLVER>' to allow for handling custom
  MILP/MIQP solvers.
• Add to the parse_soln() method of opt_model an optional stash
  input argument that, if present and true, causes the parsed
  solution to be stored back in the object, as the solve() method
  was already doing when opt.parse_soln is true.
• New Sphinx-based Reference documentation.
• New functions:
  • convert_lin_constraint() converts linear constraints from a
    single set of doubly-bounded inequality constraints to separate
    sets of equality and upper-bounded inequality constraints.
  • convert_lin_constraint_multipliers() converts multipliers on
    linear constraints from separate sets for equality and
    upper-bounded inequality constraints to those for doubly-bounded
    inequality constraints.
• New opt_model methods:
  • is_solved() indicates whether the model has been solved.
  • has_parsed_soln() indicates whether a parsed solution is
    available in the model.
  • display_soln() display the results of a solved model,
    including values, bounds and shadow prices for variables and
    linear constraints, values and shadow prices for nonlinear
    constraints, and individual cost components.

Bugs Fixed:

• Clear cached parameters after updating linear constraints or
  quadratic costs via opt_model.set_params().
• In miqps_mosek() the lower and upper bounds of binary variables
  got overwritten with 0 and 1, respectively, effectively relaxing
  any potentially tighter bounds provided as input.
• Fix false positive in have_feature_fsolve in case where the file
  is present, but without a valid license.

Other Changes:

• Update for compatibility with MATLAB R2023a (Optimization Toolbox
  9.5) and later, which removed x0 as a valid input to linprog.

• Update have_feature_ipopt() to recognize IPOPT MEX installations
  from Enrico Bertolazzi's mexIPOPT, which include MEX files
  that have been renamed to architecture-specific names along with an
  ipopt.m wrapper function to call the appropriate one.
  Thanks to Carlos Murillo-Sánchez.

  Note: While MP-Opt-Model no longer requires this, my
  recommendation is still to simply rename the MEX file to
  ipopt.<mexext>, with the appropriate architecture-specific
  extension, and delete the unnecessary ipopt.m entirely.

• Always skip price computation stage in miqps_<solver>() functions
  for pure (as opposed to mixed) integer problems.

• Add caching of aggregate output parameters in
  opt_model.params_var().