Use optionstable to display options and informs tables (#216)

* moved default options and informs to staticmethod

* updated SNOPT docs

* updated ALPSO

* updated SLSQP

* updated PSQP

* updated NLPQLP

* updated NSGA2

* updated IPOPT

* updated CONMIN

* removed optimizertable

* bugfix after changing option from '' to EXT

* updated informs

* removed options description in comments

* pin a newer version of sphinx_mdolab_theme

* version bump

doc/conf.py

@@ -21,7 +21,7 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions.extend(["numpydoc", "ext.optimizertable", "sphinxcontrib.bibtex"])
+extensions.extend(["numpydoc", "sphinxcontrib.bibtex"])
 
 # mock import for autodoc
 autodoc_mock_imports = ["baseclasses", "scipy", "numpy", "sqlitedict"]

doc/optimizers/ALPSO.rst

@@ -6,13 +6,8 @@ Augmented Lagrangian Particle Swarm Optimizer (ALPSO) is a PSO method that uses
 
 Options
 -------
-.. optimizertable:: pyoptsparse.pyALPSO.pyALPSO.ALPSO
-   :type: options
-
-Informs
--------
-.. optimizertable:: pyoptsparse.pyALPSO.pyALPSO.ALPSO
-   :type: informs
+.. optionstable:: pyoptsparse.pyALPSO.pyALPSO.ALPSO
+   :filename: optimizers/ALPSO_options.yaml
 
 API
 ---

doc/optimizers/ALPSO_options.yaml

@@ -0,0 +1,70 @@
+SwarmSize:
+  desc: Number of Particles (Depends on Problem dimensions)
+maxOuterIter:
+  desc: Maximum Number of Outer Loop Iterations (Major Iterations)
+maxInnerIter:
+  desc: Maximum Number of Inner Loop Iterations (Minor Iterations)
+minInnerIter:
+  desc: Minimum Number of Inner Loop Iterations (Dynamic Inner Iterations)
+dynInnerIter:
+  desc: Dynamic Number of Inner Iterations Flag
+stopCriteria:
+  desc: Stopping Criteria Flag (0 - maxIters, 1 - convergence)
+stopIters:
+  desc: Consecutive Number of Iterations for which the Stopping Criteria must be Satisfied
+etol:
+  desc: Absolute Tolerance for Equality constraints
+itol:
+  desc: Absolute Tolerance for Inequality constraints
+rtol:
+  desc: Relative Tolerance for Lagrange Multipliers
+atol:
+  desc: Absolute Tolerance for Lagrange Function
+dtol:
+  desc: Relative Tolerance in Distance of All Particles to Terminate (GCPSO)
+printOuterIters:
+  desc: Number of Iterations Before Print Outer Loop Information
+printInnerIters:
+  desc: Number of Iterations Before Print Inner Loop Information
+rinit:
+  desc: Initial Penalty Factor
+xinit:
+  desc: Initial Position Flag (0 - no position, 1 - position given)
+vinit:
+  desc: Initial Velocity of Particles in Normalized [-1, 1] Design Space
+vmax:
+  desc: Maximum Velocity of Particles in Normalized [-1, 1] Design Space
+c1:
+  desc: Cognitive Parameter
+c2:
+  desc: Social Parameter
+w1:
+  desc: Initial Inertia Weight
+w2:
+  desc: Final Inertia Weight
+ns:
+  desc: Number of Consecutive Successes in Finding New Best Position of Best Particle Before Search Radius will be Increased (GCPSO)
+nf:
+  desc: Number of Consecutive Failures in Finding New Best Position of Best Particle Before Search Radius will be Increased (GCPSO)
+dt:
+  desc: Time step
+vcrazy:
+  desc: Craziness Velocity (Added to Particle Velocity After Updating the Penalty Factors and Langangian Multipliers)
+fileout:
+  desc: Flag to Turn On Output to filename
+filename:
+  desc: We could probably remove fileout flag if filename or fileinstance is given
+seed:
+  desc: Random Number Seed (0 - Auto-Seed based on time clock)
+HoodSize:
+  desc: Number of Neighbours of Each Particle
+HoodModel:
+  desc: Neighbourhood Model (dl/slring - Double/Single Link Ring, wheel - Wheel, Spatial - based on spatial distance, sfrac - Spatial Fraction)
+HoodSelf:
+  desc: Selfless Neighbourhood Model (0 - Include Particle i in NH i, 1 - Don't Include Particle i)
+Scaling:
+  desc: Design Variables Scaling Flag (0 - no scaling, 1 - scaling between [-1, 1])
+parallelType:
+  desc: Type of parallelization
+  null: No parallel function evaluations
+  EXT: Use parallel function evaluations

doc/optimizers/CONMIN.rst

@@ -6,13 +6,9 @@ CONstrained function MINimization (CONMIN) is a gradient-based optimizer that us
 
 Options
 -------
-.. optimizertable:: pyoptsparse.pyCONMIN.pyCONMIN.CONMIN
-   :type: options
+.. optionstable:: pyoptsparse.pyCONMIN.pyCONMIN.CONMIN
+   :filename: optimizers/CONMIN_options.yaml
 
-Informs
--------
-.. optimizertable:: pyoptsparse.pyCONMIN.pyCONMIN.CONMIN
-   :type: informs
 
 API
 ---

doc/optimizers/CONMIN_options.yaml

@@ -0,0 +1,16 @@
+ITMAX:
+  desc: Maximum Number of Iterations
+DELFUN:
+  desc: Objective Relative Tolerance
+DABFUN:
+  desc: Objective Absolute Tolerance
+ITRM:
+  desc: None
+NFEASCT:
+  desc: None
+IPRINT:
+  desc: Print Control (0 - None, 1 - Final, 2,3,4 - Debug)
+IOUT:
+  desc: Output Unit Number
+IFILE:
+  desc: Output File Name

doc/optimizers/IPOPT.rst

@@ -82,12 +82,13 @@ Please refer to the `IPOPT website <https://coin-or.github.io/Ipopt/OPTIONS.html
 The following are the options which are set by default within pyOptSparse.
 All other options take the default value with IPOPT unless specified by the user.
 
-.. optimizertable:: pyoptsparse.pyIPOPT.pyIPOPT.IPOPT
-   :type: options
+.. optionstable:: pyoptsparse.pyIPOPT.pyIPOPT.IPOPT
+   :filename: optimizers/IPOPT_options.yaml
+
 
 Informs
 -------
-.. optimizertable:: pyoptsparse.pyIPOPT.pyIPOPT.IPOPT
+.. optionstable:: pyoptsparse.pyIPOPT.pyIPOPT.IPOPT
    :type: informs
 
 API

doc/optimizers/IPOPT_options.yaml

@@ -0,0 +1,8 @@
+print_level:
+  desc: Printing level
+output_file:
+  desc: The name of the output file from IPOPT
+option_file_name:
+  desc: The name of the options file passed to IPOPT
+linear_solver:
+  desc: The linear solver used.

doc/optimizers/NLPQLP.rst

@@ -17,12 +17,13 @@ The latest version supported is v4.2.2.
 
 Options
 -------
-.. optimizertable:: pyoptsparse.pyNLPQLP.pyNLPQLP.NLPQLP
-   :type: options
+.. optionstable:: pyoptsparse.pyNLPQLP.pyNLPQLP.NLPQLP
+   :filename: optimizers/NLPQLP_options.yaml
+
 
 Informs
 -------
-.. optimizertable:: pyoptsparse.pyNLPQLP.pyNLPQLP.NLPQLP
+.. optionstable:: pyoptsparse.pyNLPQLP.pyNLPQLP.NLPQLP
    :type: informs
 
 API

doc/optimizers/NLPQLP_options.yaml

@@ -0,0 +1,31 @@
+accuracy:
+  desc: Convergence accuracy
+accuracyQP:
+  desc: Convergence accuracy for QP
+stepMin:
+  desc: Minimum step length
+maxFun:
+  desc: Maximum Number of Function Calls During Line Search
+maxIt:
+  desc: Maximum Number of Iterations
+maxNM:
+  desc: Maximum stack size for non-monotone line search
+rho:
+  desc: Factor scaling identify for IFAIL=2
+iPrint:
+  desc: Output Level
+  0: None
+  1: Final
+  2: Major
+  3: Major/Minor
+  4: Full
+mode:
+  desc: Mode (0 - Normal Execution, 1 to 18 - See Manual)
+iOut:
+  desc: Output Unit Number
+lMerit:
+  desc: Merit Function Type (True - L2 Augmented Penalty, False - L1 Penalty)
+lQl:
+  desc: QP Subproblem Solver (True - Quasi-Newton, False - Cholesky)
+iFile:
+  desc: Output File Name

doc/optimizers/NSGA2.rst

@@ -16,13 +16,9 @@ selection-based strategy
 
 Options
 -------
-.. optimizertable:: pyoptsparse.pyNSGA2.pyNSGA2.NSGA2
-   :type: options
+.. optionstable:: pyoptsparse.pyNSGA2.pyNSGA2.NSGA2
+   :filename: optimizers/NSGA2_options.yaml
 
-Informs
--------
-.. optimizertable:: pyoptsparse.pyNSGA2.pyNSGA2.NSGA2
-   :type: informs
 
 API
 ---

doc/optimizers/NSGA2_options.yaml

@@ -0,0 +1,22 @@
+PopSize:
+  desc: Population size
+maxGen:
+  desc: Maximum number of generations
+pCross_real:
+  desc: Probability of crossover of real variables
+pMut_real:
+  desc: Probability of mutation of real variables
+eta_c:
+  desc: Distribution index for crossover
+eta_m:
+  desc: Distribution index for mutation
+pCross_bin:
+  desc: Probability of crossover of binary variable
+pMut_bin:
+  desc: Probability of mutation of binary variables
+PrintOut:
+  desc: Flag to turn on output to filename
+seed:
+  desc: Random Number Seed (0 - Auto-Seed based on time clock)
+xinit:
+  desc: Use Initial Solution Flag (0 - random population, 1 - use given solution)