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Name Modified Size InfoDownloads / Week
ilt_sos.zip 2025-06-30 117.8 MB
e2m_tcm_sos.zip 2023-11-30 17.4 MB
e2m_sos_nlp.zip 2023-11-30 117.8 MB
readme.txt 2023-07-30 1.9 kB
aeroassist_gnuplot.gnu 2023-02-16 18.2 kB
nbody_flyby.zip 2022-08-27 20.8 MB
itcm_sos.zip 2022-05-27 117.4 MB
ipto_sos.zip 2022-05-27 117.4 MB
flyby_sos.zip 2022-05-27 117.4 MB
tlto_sos.zip 2022-05-27 117.4 MB
tlto_sos_nlp.zip 2022-05-27 117.4 MB
rendezvous_sos.zip 2022-05-27 2.9 MB
reentry_sos.zip 2022-05-27 2.9 MB
leo2geo_sos.zip 2022-05-27 2.9 MB
lascent_sos.zip 2022-05-27 2.9 MB
e2m_sos.zip 2022-05-27 117.4 MB
deorbit_sos.zip 2022-05-27 3.0 MB
hyper_sos.zip 2022-05-27 3.0 MB
escape_sos.zip 2022-05-27 2.9 MB
twoburn_sos.zip 2022-05-27 3.0 MB
oneburn_sos.zip 2022-05-27 2.9 MB
aeroassist_sos.zip 2022-05-27 3.0 MB
Computer Methods for Aerospace Trajectory Optimization.pdf 2022-04-07 9.5 MB
Totals: 23 Items   1.0 GB 4
This project is a suite of computer programs called Computer Methods for Aerospace Trajectory Analysis (CMATO)
for solving practical problems in aerospace trajectory optimization.  All programs are written in Fortran and
utilize the Sparse Optimization Suite (SOS) software.

The zipped archive for each application contains the source code, several example simulation definition files
and a Windows-compatible executable program. These programs were compiled using version 11.1 of Intel Fortran.

The Sparse Optimization Suite is a direct transcription method that can be used to solve a variety of trajectory
optimization problems using the following combination of numerical methods

• collocation and implicit integration

• adaptive mesh refinement

• sparse nonlinear programming

The CMATO software consists of Fortran routines that perform the following tasks.

• set algorithm control parameters and call the transcription/optimal control subroutine

• define the problem structure and perform initialization related to scaling, lower and upper bounds,
  initial conditions, constraints, etc.

• compute the right-hand-side differential-algebraic equations

• evaluate any point and path constraints

• display the optimal solution results and create an output file

SOS will use this information to automatically transcribe the user’s optimal control problem and perform the 
optimization using a sparse nonlinear programming (NLP) method selected by the user.  

Additional information about the mathematical techniques and numerical methods used in the Sparse Optimization
Suite can be found in the book, Practical Methods for Optimal Control Using Nonlinear Programming, Third Edition,
by John. T. Betts, SIAM, 2020.

Documentation for each application can be found in the CMATO PDF file.
Source: readme.txt, updated 2023-07-30