Changes to pyOptSparse

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

• Elimination of 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 nProc instances)
• 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 subblocks
• Automatic conversion of sparse jacobian to dense for optimizers that cannot handle sparse constraints
• User specification of design variable scaling
• User specificaiton 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 naitve python shelve database format. Much easier to retrieve optimization data after optimization.
• 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 evlaution, not the gradient.
• Various bug fixes in SNOPT
• Constraints can be any order, independent of what the individual optimizer requires
• Python 3.x compatibility