Mathematical Sciences: Projected Newton Methods for Optimal Control Problems and Other Large-Scale Structured Nonlinear Programs
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{'SignBlockName': 'Michael Steuerwalt', 'PO_EMAI': None, 'PO_PHON': None}
The investigation proposed here has as its main objective the               design, analysis, and testing of the so called projected Newton                 methods in computational optimization, with a special emphasis on               the application of these methods to optimal control problems. Two               kinds of problems are considered: disrete-time dynamical                        optimization problems and continuous time problems. It seems                    likely that the results of this research may be also applicable                 to other large-scale structured nonlinear optimization problems.                    The projected Newton method algorithms converge more rapidly                than the standard first order, i.e. gradient based methods, yet                 the associated per iteration computational costs are still only                 proportional to n, where n is the number of subintervals of the                 time interval under consideration. A sizeable portion of the per                iteration work can be organized for parallel computation.                           This research belongs to computational methods in                           optimization and control. Problems of this type arise in many                   applications, typically in optimization of trajectories for space               vehicles and in calculations of optimal programs for control                    computers for various industrial processes.
{'FirstName': 'Joseph', 'LastName': 'Dunn', 'PI_MID_INIT': 'C', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Joseph C Dunn', 'EmailAddress': 'joe_dunn@ncsu.edu', 'NSF_ID': '000169920', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '1266', 'Text': 'APPLIED MATHEMATICS'}, {'Code': '1271', 'Text': 'COMPUTATIONAL MATHEMATICS'}]
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