On the approximation of infinite optimization problems with an application to optimal control problems

This paper is concerned with approximations to infinite optimization problems in Banach spaces. Under the assumption of a first order necessary and a second order sufficient optimality condition we derive convergence results for the optimal solutions and the optimal values of the approximating problems. An application to finite difference approximations of nonlinear optimal control problems with state constraints is given.     

Realtime control of robots with initial value perturbations via nonlinear programming methods

The mathematical model of an industrial robot with initial value perturbations is considered as a parametric nonlinear control problem subject to control and state constraints. Based on recent stability results for parametric control problems, a robust nonlinear programming method is proposed for computing the sensitivity derivatives of optimal solutions. Real-time control approximations of perturbed optimal solutions are obtained by evaluating a first order Taylor expansion of the perturbed solution. The efficiency of the real-time approximation is demonstrated for the robot model     

Methods for estimating the optimal dividend barrier and the probability of ruin

In applications of collective risk theory, complete information about the individual claim amount distribution is often not known, but reliable estimates of its first few moments may be available. For such a situation, this paper develops methods for estimating the optimal dividend barrier and the probability of ruin. In particular, two De Vylder approximations are explained, and the first and second order diffusion approximations are examined. For several claim amount distributions, the approximate values are compared numerically with the exact values. The De Vylder and diffusion approximations can be adapted to the more general situation where the aggregate claims process is a Lévy process with nonnegative increments.     

Optimal Control of Differential–Algebraic Equations of Higher Index,Part 2: Necessary Optimality Conditions

This paper deals with optimal control problems described by higher index DAEs. We introduce a class of problems which can be transformed to index one control problems. For these problems we show in the accompanying paper that, if the solutions to the adjoint equations are well–defined, then the first-order approximations to the functionals defining the problem can be expressed in terms of the adjoint variables. In this paper we show that the solutions to the adjoint equations are essentially bounded measurable functions. Then, based on the first order approximations, we derive the necessary optimality conditions for the considered class of control problems. These conditions do not require the transformation of the DAEs to index-one system; however, higher-index DAEs and their associated adjoint equations have to be solved.     

Analysis and approximation of optimal control problems for a simplified Ginzburg-Landau model of superconductivity

Summary. This paper is concerned with optimal control problems for a Ginzburg-Landau model of superconductivity that is valid for high values of the Ginzburg-Landau parameter and high external fields. The control is of Neumann type. We first show that optimal solutions exist. We then show that Lagrange multipliers may be used to enforce the constraints and derive an optimality system from which optimal states and controls may be deduced. Then we define finite element approximations of solutions for the optimality system and derive error estimates for the approximations. Finally, we report on some numerical results. Received May 3, 1994 / Revised version received November 28, 1995     

Superconvergence and a posteriori error estimates of RT1 mixed methods for elliptic control problems with an integral constraint

In this paper, we investigate the superconvergence property and a posteriori error estimates of mixed finite element methods for a linear elliptic control problem with an integral constraint. The state and co-state are approximated by the order k = 1 Raviart-Thomas mixed finite element spaces and the control variable is approximated by piecewise constant functions. Approximations of the optimal control of the continuous optimal control problem will be constructed by a projection of the discrete adjoint state. It is proved that these approximations have convergence order h ². Moreover, we derive a posteriori error estimates both for the control variable and the state variables. Finally, a numerical example is given to demonstrate the theoretical results.     

Optimal Planar Interception with Fixed End Conditions: Approximate Solutions

In a previous paper (Ref. 1), an exact solution of the optimal planar interception with fixed end conditions was derived in closed form. The optimal control was expressed as an explicit function of the state variables and two fixed parameters, obtained by solving a set of nonlinear algebraic equations involving elliptic integrals. In order to facilitate the optimal control implementation, the present paper derives a highly accurate simplified solution assuming that the ratio of the pursuer turning radius to the initial range is small. An asymptotic expansion further reduces the computational workload. Construction of a near-optimal open-loop control, based on the approximations, completes the present paper.     

OPTIMAL CONTROL OF MULTIPLE-USE PRODUCTS: THE CASE OF TIMBER,FORAGE AND WATER PRODUCTION

ABSTRACT. A thorough analysis of the optimal control of multiple-use forest management at the stand level reveals that the results of earlier studies, which seem to contradict each other, are in fact part of a common solution space. We provide an explanation for this result by showing that it is caused by the growth function and the interaction between the timber and forage production functions. We discuss the sensibility of the results using this new knowledge. Most optimal control models focusing on multiple-use forest management have applied production functions that are quadratic in the state variable. This makes explicit solutions easy because the first order derivative is linear. However, in reality, production is often better described by more complex nonlinear functions, but, unfortunately, such functions are difficult to handle in an optimal control framework. We illustrate how the convenience of the quadratic production function can be combined with better approximations to nonlinear production functions.     

A one-step optimal homotopy analysis method for nonlinear differential equations

In this paper, a one-step optimal approach is proposed to improve the computational efficiency of the homotopy analysis method (HAM) for nonlinear problems. A generalized homotopy equation is first expressed by means of a unknown embedding function in Taylor series, whose coefficient is then determined one by one by minimizing the square residual error of the governing equation. Since at each order of approximation, only one algebraic equation with one unknown variable is solved, the computational efficiency is significantly improved, especially for high-order approximations. Some examples are used to illustrate the validity of this one-step optimal approach, which indicate that convergent series solution can be obtained by the optimal homotopy analysis method with much less CPU time. Using this one-step optimal approach, the homotopy analysis method might be applied to solve rather complicated differential equations with strong nonlinearity.     

Optimal boundary control of glass cooling processes

In this paper, an optimal control problem for glass cooling processes is studied. We model glass cooling using the SP1 approximations to the radiative heat transfer equations. The control variable is the temperature at the boundary of the domain. This results in a boundary control problem for a parabolic/elliptic system which is treated by a constrained optimization approach. We consider several cost functionals of tracking‐type and formally derive the first‐order optimality system. Several numerical methods based on the adjoint variables are investigated. We present results of numerical simulations illustrating the feasibility and performance of the different approaches. Copyright © 2004 John Wiley & Sons, Ltd.     

A Stability Theorem for Constrained Optimal Control Problems

This paper presents the stability of difference approximations of an optimal control problem for a quasilinear parabolic equation with controls in the coefficients, boundary conditions and additional restrictions. The optimal control problem has been convered to one of the optimization problem using a penalty function technique. The difference approximations problem for the considered problem is obtained. The estimations of stability of the solution of difference approximations problem are proved. The stability estimation of the solution of difference approximations problem by the controls is obtained.     

A simple procedure for determining order quantities under a fill rate constraint and normally distributed lead-time demand

One of the most common practical inventory control problems is considered. A single-echelon inventory system is controlled by a continuous review (R, Q) policy. The lead-time demand is normally distributed. We wish to minimize holding and ordering costs under a fill rate constraint. Although, it is not especially complicated to derive the optimal solution, it is much more common in practice to use a simple approximate two-step procedure where the order quantity is determined from a deterministic model in the first step. We provide an alternative, equally simple technique, which is based on the observation that the considered problem for each considered fill rate has a single parameter only. The optimal solution for a grid of parameter values is stored in a file. When solving the problem for an item we use interpolation, or for parameter values outside the grid special approximations. The approximation errors turn out to be negligible. As an alternative to the interpolation we also provide polynomial approximations.     

Interior variations in dynamic optimization problems

In this article, the author examines the properties of interior variations and indicates how to use them in order to formulate the necessary condition of optimality for problems of dynamic optimization, in particular, problems of variational calculus and of optimal control. For optimal control problems, an optimization technique based on interior variations and polynomial approximations is suggested and then illustrated by an explanatory example.     

Epi-convergent discretization of the generalized Bolza problem in dynamic optimization

The paper is devoted to well-posed discrete approximations of the so-called generalized Bolza problem of minimizing variational functionals defined via extended-real-valued functions. This problem covers more conventional Bolza-type problems in the calculus of variations and optimal control of differential inclusions as well of parameterized differential equations. Our main goal is find efficient conditions ensuring an appropriate epi-convergence of discrete approximations, which plays a significant role in both the qualitative theory and numerical algorithms of optimization and optimal control. The paper seems to be the first attempt to study epi-convergent discretizations of the generalized Bolza problem; it establishes several rather general results in this direction. Research of B. S. Mordukhovich was partially supported by the USA National Science Foundation under grants DMS-0304989 and DMS-0603846 and by the Australian Research Council under grant DP-0451168. Research of T. Pennanen was supported by the Finnish Academy of Sciences under contract No. 3385.     

A receding horizon control approach for integrated vector management of Aedes aegypti using chemical and biological control: A mono and a multiobjective approach

This paper addresses an integrated vector management (IVM) approach for combating Aedes aegypti, the transmission vector of dengue, zika, and chikungunya diseases, some of the most important viral epidemics worldwide. In order to tackle this problem, a receding horizon control (RHC) strategy is adopted, considering a mono-objective and a multiobjective version of the optimal control model of combating the mosquito using chemical and biological control. RHC is essentially a suboptimal scheme of classical optimal control strategies considering discrete-time approximations. The integrated vector control actions used in this work consist in applying insecticides and inserting sterile males produced by irradiation in the population of mosquitoes. The cost function is defined in terms of social and economic costs, in order to quantify the effectiveness of the proposed epidemiological control throughout a time window of 4 months. Numerical simulations show that the obtained results are better than those from the optimal control strategies found in literature. Furthermore, through the application of the multiobjetive approach, varying the scenarios in the mono-objective formulation is no longer necessary and a set of optimal strategies can be obtained at once. Finally, in order to help health authorities in the choice of the best solution of the Pareto-optimal set to be implemented in practice, a cost-effectiveness analysis is performed and a strategy representing the most cost-effective control policy is obtained.     

On the bailout dividend problem with periodic dividend payments for spectrally negative Markov additive processes

This paper studies the bailout optimal dividend problem with regime switching under the constraint that dividend payments can be made only at the arrival times of an independent Poisson process while capital can be injected continuously in time. We show the optimality of the regime-modulated Parisian-classical reflection strategy when the underlying risk model follows a general spectrally negative Markov additive process. In order to verify the optimality, first we study an auxiliary problem driven by a single spectrally negative Lévy process with a final payoff at an exponential terminal time and characterize the optimal dividend strategy. Then, we use the dynamic programming principle to transform the global regime-switching problem into an equivalent local optimization problem with a final payoff up to the first regime switching time. The optimality of the regime modulated Parisian-classical barrier strategy can be proven by using the results from the auxiliary problem and approximations via recursive iterations.     

Closed-form expressions for the finite difference approximations of first and higher derivatives based on Taylor series

Numerical differentiation formulas based on interpolating polynomials, operators and lozenge diagrams can be simplified to one of the finite difference approximations based on Taylor series. In this paper, we have presented closed-form expressions of these approximations of arbitrary order for first and higher derivatives. A comparison of the three types of approximations is given with an ideal digital differentiator by comparing their frequency responses. The comparison reveals that the central difference approximations can be used as digital differentiators, because they do not introduce any phase distortion and their amplitude response is closer to that of an ideal differentiator. It is also observed that central difference approximations are in fact the same as maximally flat digital differentiators. In the appendix, a computer program, written in MATHEMATICA is presented, which can give the approximation of any order to the derivative of a function at a certain mesh point.     

Linear Programming Approximations for Markov Control Processes in Metric Spaces

We develop a general framework to analyze the convergence of linear-programming approximations for Markov control processes in metric spaces. The approximations are based on aggregation and relaxation of constraints, as well as inner approximations of the decision variables. In particular, conditions are given under which the control problems optimal value can be approximated by a sequence of finite-dimensional linear programs.     

Taylor expansions of the value function associated with a bilinear optimal control problem

A general bilinear optimal control problem subject to an infinite-dimensional state equation is considered. Polynomial approximations of the associated value function are derived around the steady state by repeated formal differentiation of the Hamilton–Jacobi–Bellman equation. The terms of the approximations are described by multilinear forms, which can be obtained as solutions to generalized Lyapunov equations with recursively defined right-hand sides. They form the basis for defining a suboptimal feedback law. The approximation properties of this feedback law are investigated. An application to the optimal control of a Fokker–Planck equation is also provided.     

Nonlinear Programming Methods for Real-Time Control of an Industrial Robot

The optimal control of an industrial robot is considered as a parametricnonlinear control problem subject to control and state constraints. Based onrecent stability results for parametric control problems, a robust nonlinearprogramming method is proposed to compute the sensitivity of open-loopcontrol solutions. Real-time control approximations of the perturbedoptimal solutions are obtained by evaluating first-order Taylor expansionsof the optimal solutions with respect to the parameter. The proposednumerical methods are applied to the industrial robot Manutec r3. Thequality of the real-time approximations is illustrated for perturbations inthe transport load.