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NASA NTRS1983

BANDIT in NASTRAN

BANDIT has been implemented into the NASTRAN April '83 release. It is now a permanent feature in NASTRAN and will be included in all future releases for all four computing machines (IBM, CDC, UNIVAC, and VAX). Originally BANDIT operated as a preprocessor of NASTRAN. It read the NASTRAN input cards and produced a set of resequencing (SEOGP) cards that would greatly reduce the computational time required by the matrix decomposition module of NASTRAN for a large structure. In the past, many computer centers had installed BANDIT in their systems together with the NASTRAN program. The user would run the BANDIT program and NASTRAN as if they were one program (or two separated programs depending on how the two programs were actually tied together in the computer systems). In some cases, the user was required to pass the input cards and the output SEQGP cards between the two programs, and n others, the data was manipulated through the use of cataloged disc files. Although there is nothing wrong with this BANDIT-NASTRAN arrangement, there are, however, several shortcomings which are mentioned in this paper. The BANDIT in the NASTRAN April '83 release has removed all the deficiencies, and it comes in one version applicable to the four computing machines.

Chan, G. C.↗
NASA NTRS1972

The BANDIT computer program for the reduction of matrix bandwidth for NASTRAN

A computer program, BANDIT, is described which was developed for use as a preprocessor to NASTRAN to automatically resequence the grid point numbers for reduced matrix bandwidth. The BANDIT program accepts a standard NASTRAN data deck as input, resequences the grid point numbers for reduced bandwidth, and generates a set of SEQGP cards for insertion into the NASTRAN bulk data deck. BANDIT uses the Cuthill-McKee strategy for resequencing grid points. The program is operational on the CDC 6000 series of computers, the IBM 360, and the Univac 1108.

Everstine, G. C.↗
NASA NTRS1975

Recent improvements to BANDIT

The NASTRAN preprocessor BANDIT, which improves NASTRAN's computer efficiency by resequencing grid point labels for reduced matrix bandwidth is described. The addition of (1) the Gibbs-Poole-Stockmeyer (GPS) algorithm, and (2) the user option to reduce matrix profile rather than matrix bandwidth is also described. It is shown that, compared to the Cuthill-McKee algorithm on which BANDIT was originally based, GPS is faster and achieves similar results. Current capabilities and options of BANDIT are summarized.

Everstine, G. C.↗
NASA NTRS2007

Proximity Navigation of Highly Constrained Spacecraft

Bandit is a 3-kg automated spacecraft in development at Washington University in St. Louis. Bandit's primary mission is to demonstrate proximity navigation, including docking, around a 25-kg student-built host spacecraft. However, because of extreme constraints in mass, power and volume, traditional sensing and actuation methods are not available. In particular, Bandit carries only 8 fixed-magnitude cold-gas thrusters to control its 6 DOF motion. Bandit lacks true inertial sensing, and the ability to sense position relative to the host has error bounds that approach the size of the Bandit itself. Some of the navigation problems are addressed through an extremely robust, error-tolerant soft dock. In addition, we have identified a control methodology that performs well in this constrained environment: behavior-based velocity potential functions, which use a minimum-seeking method similar to Lyapunov functions. We have also adapted the discrete Kalman filter for use on Bandit for position estimation and have developed a similar measurement vs. propagation weighting algorithm for attitude estimation. This paper provides an overview of Bandit and describes the control and estimation approach. Results using our 6DOF flight simulator are provided, demonstrating that these methods show promise for flight use.

Scarritt, S.↗
NASA NTRS1989

Improved performance in NASTRAN (R)

Three areas of improvement in COSMIC/NASTRAN, 1989 release, were incorporated recently that make the analysis program run faster on large problems. Actual log files and actual timings on a few test samples that were run on IBM, CDC, VAX, and CRAY computers were compiled. The speed improvement is proportional to the problem size and number of continuation cards. Vectorizing certain operations in BANDIT, makes BANDIT run twice as fast in some large problems using structural elements with many node points. BANDIT is a built-in NASTRAN processor that optimizes the structural matrix bandwidth. The VAX matrix packing routine BLDPK was modified so that it is now packing a column of a matrix 3 to 9 times faster. The denser and bigger the matrix, the greater is the speed improvement. This improvement makes a host of routines and modules that involve matrix operation run significantly faster, and saves disc space for dense matrices. A UNIX version, converted from 1988 COSMIC/NASTRAN, was tested successfully on a Silicon Graphics computer using the UNIX V Operating System, with Berkeley 4.3 Extensions. The Utility Modules INPUTT5 and OUTPUT5 were expanded to handle table data, as well as matrices. Both INPUTT5 and OUTPUT5 are general input/output modules that read and write FORTRAN files with or without format. More user informative messages are echoed from PARAMR, PARAMD, and SCALAR modules to ensure proper data values and data types being handled. Two new Utility Modules, GINOFILE and DATABASE, were written for the 1989 release. Seven rigid elements are added to COSMIC/NASTRAN. They are: CRROD, CRBAR, CRTRPLT, CRBE1, CRBE2, CRBE3, and CRSPLINE.

Chan, Gordon C.↗
NASA NTRS1978

Reduction of matrix wavefront for NASTRAN

The three grid point resequencing algorithms most often run by NASTRAN users are compared for their ability to reduce matrix root-mean-square (rms) wavefront, which is the most critical parameter in determining matrix decomposition time in NASTRAN. The three algorithms are Cuthill-McKee (CM), Gibbs-Poole-Stockmeyer (GPS), and Levy. The first two (CM and GPS) are in the BANDIT program, and the Levy algorithm is in WAVEFRONT. Results are presented for a diversified collection of 30 test problems ranging in size from 59 to 2680 nodes. It is concluded that GPS is exceptionally fast and, for the conditions under which the test was made, the algorithm best able to reduce rms wavefront consistently well.

Everstine, G. C.↗
NASA NTRS2005

Coevolutionary Free Lunches

Recent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the "No Free Lunch" (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering most search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving agents. As a particular instance of the latter, it covers "self-play" problems. In these problems the agents work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. However in the typical coevolutionary scenarios encountered in biology, where there is no champion, NFL still holds.

Wolpert, David H.↗
NASA NTRS2005

Coevolutionary Free Lunches

Recent work on the mathematical foundations of optimization has begun to uncover its rich structure. In particular, the "No Free Lunch" (NFL) theorems state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering more search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving players. As a particular instance of the latter, it covers "self-play" problems. In these problems the set of players work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game. In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. We consider the implications of these results to biology where there is no champion.

Wolpert, David H.↗
NASA NTRS1978

Comsat Antenna

The antenna shown is the new, multiple-beam, Unattended Earth Terminal, located at COMSAT Laboratories in Clarksburg, Maryland. Seemingly simple, it is actually a complex structure capable of maintaining contact with several satellites simultaneously (conventional Earth station antennas communicate with only one satellite at a time). In developing the antenna, COMSAT Laboratories used NASTRAN, NASA's structural analysis computer program, together with BANDIT, a companion program. The computer programs were used to model several structural configurations and determine the most suitable, The speed and accuracy of the computerized design analysis afforded appreciable savings in time and money.

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