The problem of model correction for sampled data models of flexible structures is addressed. The problem of finding a feed-through term to compensate for the effect of truncated higher-frequency modes on in-bandwidth ...
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The problem of model correction for sampled data models of flexible structures is addressed. The problem of finding a feed-through term to compensate for the effect of truncated higher-frequency modes on in-bandwidth dynamics of the system was set up as an H2 optimization problem, and an analytical solution to this problem was *** correction for sampled-data models of structures (CSA)
This paper deals with the numerical approximation for the time optimal control problem governed by the Benjamin-Bona-Mahony (BBM) equation, which is an unspecified terminal time problem. Firstly, by projecting the ori...
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This paper deals with the numerical approximation for the time optimal control problem governed by the Benjamin-Bona-Mahony (BBM) equation, which is an unspecified terminal time problem. Firstly, by projecting the original problem with the finite element method (FEM), another approximate problem governed by a system of ordinary differential equations will be obtained. Then, the parameterisation method for the optimal time and the control function will be carried out and the unspecified terminal time problem can be reduced to an optimal parameter selection problem with a fixed time horizon [0, 1]. This optimal parameter selection problem is a standard nonlinear mathematical programming problem and can be solved by sequential quadratic programming (SQP) algorithm. Finally, some numerical simulation studies will be given to illustrate the effectiveness of our numerical approximation method for the time optimal control problem governed by the BBM equation.
We derive new quasi-Newton updates for the (nonlinear) equality constrained minimization problem. The new updates satisfy a quasi-Newton equation, maintain positive definiteness on the null space of the active constra...
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We derive new quasi-Newton updates for the (nonlinear) equality constrained minimization problem. The new updates satisfy a quasi-Newton equation, maintain positive definiteness on the null space of the active constraint matrix, and satisfy a minimum change condition. The application of the updates is not restricted to a small neighbourhood of the solution. In addition to derivation and motivational remarks, we discuss various numerical subleties and provide results of numerical experiments.
sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems wit...
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sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints ( linear and nonlinear). We assume that first derivatives are available and that the constraint gradients are sparse. We discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems. SNOPT is a particular implementation that makes use of a semidefinite QP solver. It is based on a limited-memory quasi-Newton approximation to the Hessian of the Lagrangian and uses a reduced-Hessian algorithm (SQOPT) for solving the QP subproblems. It is designed for problems with many thousands of constraints and variables but a moderate number of degrees of freedom ( say, up to 2000). An important application is to trajectory optimization in the aerospace industry. Numerical results are given for most problems in the CUTE and COPS test collections ( about 900 examples).
A new design method that satisfies the setting settling time with small maximum overshoot in a servo controller was developed using a PI controller and an internal feedback system. The internal feedback system consist...
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We introduce the optimizer CSOLNP, which is a C++ implementation of the R package RSOLNP (Ghalanos & Theussl, 2012, Rsolnp: General non-linear optimization using augmented Lagrange multiplier method. R package ver...
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We introduce the optimizer CSOLNP, which is a C++ implementation of the R package RSOLNP (Ghalanos & Theussl, 2012, Rsolnp: General non-linear optimization using augmented Lagrange multiplier method. R package version, 1) alongside some improvements. CSOLNP solves non-linearly constrained optimization problems using a sequential quadratic programming (SQP) algorithm. CSOLNP, NPSOL (a very popular implementation of SQP method in FORTRAN (Gill et al., 1986, User's guide for NPSOL (version 4.0): A Fortran package for nonlinear programming (No. SOL-86-2). Stanford, CA: Stanford University Systems Optimization Laboratory), and SLSQP (another SQP implementation available as part of the NLOPT collection (Johnson, 2014, The NLopt nonlinear-optimization package. Retrieved from http://***/nlopt)) are three optimizers available in OpenMx package. These optimizers are compared in terms of runtimes, final objective values, and memory consumption. A Monte Carlo analysis of the performance of the optimizers was performed on ordinal and continuous models with five variables and one or two factors. While the relative difference between the objective values is less than 0.5%, CSOLNP is in general faster than NPSOL and SLSQP for ordinal analysis. As for continuous data, none of the optimizers performs consistently faster than the others. In terms of memory usage, we used Valgrind's heap profiler tool, called Massif, on one-factor threshold models. CSOLNP and NPSOL consume the same amount of memory, while SLSQP uses 71 MB more memory than the other two optimizers.
In Nonlinear Model Predictive Control(NMPC), an optimal control problem (OCP) is solved repeatedly at every sampling instant. To satisfy the real-time restriction, modern methods tend to convert the OCP into structure...
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The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve th...
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The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration. Both primal and primal-dual versions of the algorithm are developed, and their performance is illustrated in a set of numerical tests.
This paper presents a discussion of "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem" Fesanghary et al. "Energy" 34 (2009) 757-766. The discus...
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This paper presents a discussion of "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem" Fesanghary et al. "Energy" 34 (2009) 757-766. The discussed paper presented economic dispatch problem by experimenting with six case studies considering 38, 13, 15, 6, and 10-unit test systems along with multi area economic dispatch (MAED) problem. However, in the reported results of case study 3 and 6, the total fuel cost calculations are different for the given generation schedule. In this discussion, clarification regarding cost calculation is presented. (C) 2012 Elsevier Ltd. All rights reserved.
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