NonLinear Parallel OPtimization Tool

The technological advancement proposed is a novel large-scale Noninear Parallel OPtimization Tool (NLPAROPT). This software package will eliminate the computational bottleneck suffered by many standard NASA-utilized analysis tools such as GMAT, EMTG and NASTRAN. Currently these programs rely on serial nonlinear programming solvers such as the Sparse Nonlinear OPTimizer (SNOPT), despite the fact that their own codebases support full parallelization. The same is true for tools used in other industries for applications such as electric power grid optimization, nuclear reactor control and stock market analysis. The NLPAROPT algorithm can be quickly incorporated into these existing software solutions via a user-friendly interface and will offer an instant runtime reduction for very large-scale optimization problems. Irrespective of runtime gains, Phase I analysis has shown that the NLPAROPT algorithm is capable of outperforming industry standard serial solvers such as SNOPT for tested problems, including complex trajectory design problems. The Phase I effort has also identified several potential computational research avenues that, once completed in Phase II, will result in massive execution speed increases, further improving the attractiveness of this new parallel algorithm.

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 parallel large-scale, nonlinear, sparse optimization solution, one which does not have its speed bottlenecked by a single processor. The new parallelized NLP technique implemented in NLPAROPT 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 GMATs collocation-based low-thrust transcription and EMTGs inner loop solver. Additionally, NLPAROPT could improve run-times across all forms of problem optimizations, including trajectory design, resource management, attitude determination and control, and vehicle design.

Government agencies other than NASA, as well as commercial markets, would benefit from the improvements inherent in NLPAROPT, especially given the widespread use of nonlinear programming techniques as a primary method for solving some of the most difficult technical computing problems. For example, in economics the product-mix with price elasticity problem can be formulated as a nonlinear program and solved with a tool like NLPAROPT. Another field that depends heavily on efficient and robust NLP solvers is operations research, with the facility location problem and network optimization problems being archetypal examples of operation research challenges that may be cast as nonlinear programs. Furthermore, industries dealing with problems such as power grid design, weather prediction, and crop planting optimization could benefit from NLPAROPT?s speed enhancements.

This is a mature technology (TRL 7) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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