This paper describes how a constrained nonlinear least-squares optimization approach can be used to recover the configuration of the segments in an arbitrary mechanical system from motion capture data. By appending the difference between markers in the model and the measured marker trajectories to the set of kinematic constraint equations, a set of over-determinate nonlinear equations will be o...

Sundance is a package in the Trilinos suite designed to provide high-level components for the development of highperformance PDE simulators with built-in capabilities for PDE-constrained optimization. We review the implications of PDE-constrained optimization on simulator design requirements, then survey the architecture of the Sundance problem specification components. These components allow i...

When an inverse problem can be formulated so the data are minima of one of the variational problems of mathematical physics, feasibility constraints can be found for the nonlinear inversion problem. These constraints guarantee that optimal solutions of the inverse problem lie in the convex feasible region of the model space. Furthermore, points on the boundary of this convex region can be found...

[Received on 31 March 2007] We propose a globalization strategy for nonlinear constrained optimization. The method employs a “flexible” penalty function to promote convergence, where during each iteration the penalty parameter can be chosen as any number within a prescribed interval, rather than a fixed value. This increased flexibility in the step acceptance procedure is designed to promote lo...

Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained mul...

This document presents a proposal to incorporate a fitness inheritance mechanism into an Evolution Strategy used to solve the general nonlinear programming problem. The aim is to find a trade-off between a lower number of evaluations of each solution and a good performance of the approach. A set of test problems taken from the specialized literature was used to test the capabilities of the prop...

State estimator design for a nonlinear discrete-time system is a challenging problem, further complicated when additional physical insight is available in the form of inequality constraints on the state variables and disturbances. One strategy for constrained state estimation is to employ online optimization using a moving horizon approximation. In this article we propose a general theory for c...

. . . If a function of several variables should be a maximum or minimum and there are between these variables one or several equations, then it will be suffice to add to the proposed function the functions that should be zero, each multiplied by an undetermined quantity, and then to look for the maximum and the minimum as if the variables were independent; the equation that one will find combin...

We present an efficient method to optimize network resource allocations under nonlinear Quality of Service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly...

A new method is proposed for solving optimization problems with equality constraints and bounds on the variables. In the spirit of Sequential Quadratic Programming and Sequential Linearly-Constrained Programming, the new method approximately solves, at each iteration, an equality-constrained optimization problem. The bound constraints are handled in outer iterations by means of an Augmented Lag...