SNOPT

SNOPT is a NLP solver developed by P. Gill University of California, San Diego along with W. Murray, and M. Saunders at Stanford University.  SNOPT is suitable for large nonlinearly constrained problems with a modest number of degrees of freedom.  SNOPT implements a sequential programming algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and in the quadratic programming sub-problems.  SNOPT can solve LP, RMIP, NLP, DNLP, and RMINLP model types.  The solver manual is on SNOPT.pdf.