Optimal Control of Differential–Algebraic Equations of Higher Index, Part 1: First-Order Approximations

This paper deals with optimal control problems described by higher index DAEs. We introduce a class of these problems which can be transformed to index one control problems. For this class of higher index DAEs, we derive first-order approximations and adjoint equations for the functionals defining the problem. These adjoint equations are then used to state, in the accompanying paper, the necessary optimality conditions in the form of a weak maximum principle. The constructive way used to prove these optimality conditions leads to globally convergent algorithms for control problems with state constraints and defined by higher index DAEs.     

Optimal scheduling of reservoir releases during flood: Deterministic optimization problem,part 1, procedure

In this paper, we are concerned with the optimal scheduling of water releases from retention reservoirs during flood, with the objective of minimizing flood damages at the important damage centers downstream of the reservoirs. Unlike in most other papers devoted to this subject, the flood routing equations are nonlinear. The performance index of the problem leads to a minimax optimal control problem. For this problem, the necessary optimality conditions are provided and a version of the feasible directions method is proposed.The research reported here has been supported by the Central Basic Research Program CPBP-03.09, Metody Analizy i Uytkowania Zasobow Wodnych, Polish Academy of Sciences, Warsaw, Poland. This support is kindly acknowledged.     

Comments on Global convergence of the method of shortest residuals by Y. Dai and Y. Yuan

Summary. We show that the example given in [Dai, Y., Yuan, Y. (1999): Global convergence of the method of shortest residuals, Numerische Mathematik 83, 581–598] does not contradict the results of [Pytlak, R. (1994): On the convergence of conjugate gradient algorithms, IMA J. Numerical Analysis 14, 443–460]. Received September 9, 2000 / Revised version received November 28, 2000 / Published online July 25, 2001     

Preconditioned conjugate gradient algorithms for nonconvex problems with box constraints

The paper describes new conjugate gradient algorithms for large scale nonconvex problems with box constraints. In order to speed up convergence the algorithms employ scaling matrices which transform the space of original variables into the space in which Hessian matrices of the problem’s functionals have more clustered eigenvalues. This is done by applying limited memory BFGS updating matrices. Once the scaling matrix is calculated, the next few conjugate gradient iterations are performed in the transformed space. The box constraints are treated efficiently by the projection. We also present a limited memory quasi-Newton method which is a special version of our general algorithm. The presented algorithms have strong global convergence properties, in particular they identify constraints active at a solution in a finite number of iterations. We believe that they are competitive to the L-BFGS-B method and present some numerical results which support our claim.     

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.     

On the Method of Shortest Residuals for Unconstrained Optimization

The paper discusses several versions of the method of shortest residuals, a specific variant of the conjugate gradient algorithm, first introduced by Lemaréchal and Wolfe and discussed by Hestenes in a quadratic case. In the paper we analyze the global convergence of the versions considered. Numerical comparison of these versions of the method of shortest residuals and an implementation of a standard Polak–Ribière conjugate gradient algorithm is also provided. It supports the claim that the method of shortest residuals is a viable technique, competitive to other conjugate gradient algorithms.     

Runge–Kutta Based Procedure for the Optimal Control of Differential-Algebraic Equations

A new approach for optimization of control problems defined by fully implicit differential-algebraic equations is described in the paper. The main feature of the approach is that system equations are substituted by discrete-time implicit equations resulting from the integration of the system equations by an implicit Runge–Kutta method. The optimization variables are parameters of piecewise constant approximations to control functions; thus, the control problem is reduced to the control space only. The method copes efficiently with problems defined by large-scale differential-algebraic equations.     

Optimal scheduling of reservoir releases during flood: Deterministic optimization problem,part 2, case study

This paper applies the optimization procedure developed in Part 1 to the problem of the optimal scheduling of reservoir releases during flood in the case study concerning the river system of Upper Vistula in Poland. Technical details related to the implementation of the proposed algorithm are discussed.The research reported here has been supported by the Central Basic Research Program CPBP-03.09, Metody Analizy i Uytkowania Aasobow Wodnych, Polish Academy of Sciences, Warsaw, Poland. This support is kindly acknowledged.     

Feasible Direction Algorithm for Optimal Control Problems with State and Control Constraints: Implementation

In this paper, we describe the implementation aspects of an optimization algorithm for optimal control problems with control, state, and terminal constraints presented in our earlier paper. The important aspect of the implementation is that, in the direction-finding subproblems, it is necessary only to impose the state constraint at relatively few points in the time involved. This contributes significantly to the algorithmic efficiency. The algorithm is applied to solve several optimal control problems, including the problem of the abort landing of an aircraft in the presence of windshear.     

Analyzing securitization processes using simulation methods

The aim of this paper is to present an analysis of securitization processes using simulation and optimization methods. We discuss the main risk factors that may affect profitability of the process. These risk factors are interest rates and mortgage prepayments. We combine latest risk factor models to create a consistent framework to analyze and improve securitization processes. We then show that making ad hoc securitization decisions may be far less efficient than by solving optimization problems. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)