NASA currently utilizes SNOPT, IPOPT, and WORHP software packages for astrodynamics applications such as the design of complex spacecraft trajectories and other optimal control problems, but could greatly benefit from the introduction of a new parallel large-scale, nonlinear, sparse optimization solution. This new parallelized NLP technique has already been shown to result in a reduction in execution time, thereby reducing the optimization's turn-around time and improve communications between both designers and scientists. Our solver would act as a significant force multiplier for existing NASA tools such as GMAT's collocation-based low-thrust transcription, and EMTGs inner loop solver.
Any group which needs to take advantage of nonlinear program problem optimization will benefit from the improved time-to-solution provided by NLPAROPT. There are direct applications to engineering design for other government agencies such as DoD who could use it in logistics optimziation, or NOAA who could use it for weather modelling. The DOE could use it for power grid optimization, and the USDA could use it for crop planting strategies. Further, most large aerospace corporations have internal optimization tools that depend on black-box solvers; NLPAROPT could substitute for those solvers and significantly improve the existing tools used in industry.