Major changes compared to pyOpt

The following list summarizes some of the changes/improvements made to pyOpt to get pyOptSparse:

• Elimination of \(\mathcal{O} (n^2)\) scaling behaviour when adding large numbers of design variables (>10,000)

• Proper handling of all optimizers when run in parallel environment (only a single optimization instance is run, not one instance per processor)

• More flexible return specification of constraints

• More flexible return specification of constraint gradients

• Complete elimination of gradient indexing errors using dictionary-based returns

• Substantial improvement of optimization script robustness through indexing elimination

• Automatic assembly of sparse Jacobians with both dense and sparse sub-blocks

• Automatic conversion of sparse Jacobian to dense for optimizers that cannot handle sparse constraints

• User specification of design variable scaling

• User specification of constraint scaling

• Design variable returns in dictionary format only

• Specification of linear constraints, dense or sparse

• Sparse non-linear Jacobians

• New history file format. Uses SQLite dictionaries.

• Fixed hot start bug where first call to user functions is a gradient. It is now guaranteed, that the first call is to the function evaluation, not the gradient.

• Various bug fixes in SNOPT

• Constraints can be in any order, independent of what the individual optimizer requires

• Python 3.x compatibility