Implementable Algorithm for Stochastic Optimization Using Sample Average Approximations
MetadataShow full item record
We develop an implementable algorithm for stochastic optimization problems involving probability functions. Such problems arise in the design of structural and mechanical systems. The algorithm consists of a nonlinear optimization algorithm applied to sample average approximations and a precision-adjustment rule. The sample average approximations are constructed using Monte Carlo simulations or importance sampling techniques. We prove that the algorithm converges to a solution with probability one and illustrate its use by an example involving a reliability-based optimal design.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Showing items related by title, author, creator and subject.
Rhoden, Christopher A. (Monterey, California. Naval Postgraduate School, 1994-06);The Simplex algorithm, developed by George B. Dantzig in 1947 represents a quantum leap in the ability of applied scientists to solve complicated linear optimization problems. Subsequently, its utility in solving finite ...
Huang, Jo-Wen (Monterey, California: Naval Postgraduate School, 2017-06);With the development and advancement in the technology of control and multi-robot systems, robot agents are likely to take over mine countermeasure (MCM) missions one day. The path planning coverage algorithm is an essential ...
Tan, Ko-Cheng (Monterey, California. Naval Postgraduate School, 1996-06);Motion planning and control of a Nomad 200 mobile robot are studied in this thesis. The objective is to develop a motion planning and control algorithm that is able to move the robot from an initial configuration (position ...