Classical Optimality Conditions Research Articles

On regularization of the classical optimality conditions in the convex optimization problems for Volterra-type systems with operator constraints

On regularization of the classical optimality conditions in the convex optimization problems for Volterra-type systems with operator constraints

On Regularization of Classical Optimality Conditions in Convex Optimization Problems for Volterra-Type Systems with Operator Constraints

On Regularization of Classical Optimality Conditions in Convex Optimization Problems for Volterra-Type Systems with Operator Constraints

Regularization of classical optimality conditions in optimization problems for linear Volterra-type systems with functional constraints

Regularization of classical optimality conditions in optimization problems for linear Volterra-type systems with functional constraints

On regularization of the Lagrange principle in the optimization problems for linear distributed Volterra type systems with operator constraints

Regularization of the classical optimality conditions - the Lagrange principle and the Pontryagin maximum principle - in a convex optimal control problem subject to functional equality and inequality constraints is considered. The controlled system is described by a linear functional-operator equation of second kind of the general form in the space $L_2^m$. The main operator on the right-hand side of the equation is assumed to be quasi-nilpotent. The objective functional to be minimized is strongly convex. The derivation of the regularized classical optimality conditions is based on the use of the dual regularization method. The main purpose of the regularized Lagrange principle and regularized Pontryagin maximum principle is to stably generate minimizing approximate solutions in the sense of J. Warga. As an application of the results obtained for the general linear functional-operator equation of second kind, two examples of concrete optimal control problems related to a system of delay equations and to an integro-differential transport equation are discussed.

On regularization of classical optimality conditions in convex optimal control

On regularization of classical optimality conditions in convex optimal control

Regularization of the Classical Optimality Conditions in Optimal Control Problems for Linear Distributed Systems of Volterra Type

Regularization of the Classical Optimality Conditions in Optimal Control Problems for Linear Distributed Systems of Volterra Type

Regularized classical optimality conditions in iterative form for convex optimization problems for distributed Volterra-type systems

We consider the regularization of the classical optimality conditions (COCs) — the Lagrange principle and the Pontryagin maximum principle — in a convex optimal control problem with functional constraints of equality and inequality type. The system to be controlled is given by a general linear functional-operator equation of the second kind in the space $L^m_2$, the main operator of the right-hand side of the equation is assumed to be quasinilpotent. The objective functional of the problem is strongly convex. Obtaining regularized COCs in iterative form is based on the use of the iterative dual regularization method. The main purpose of the regularized Lagrange principle and the Pontryagin maximum principle obtained in the work in iterative form is stable generation of minimizing approximate solutions in the sense of J. Warga. Regularized COCs in iterative form are formulated as existence theorems in the original problem of minimizing approximate solutions. They “overcome” the ill-posedness properties of the COCs and are regularizing algorithms for solving optimization problems. As an illustrative example, we consider an optimal control problem associated with a hyperbolic system of first-order differential equations.

Принцип Лагранжа и его регуляризация как теоретическая основа устойчивого решения задач оптимального управления и обратных задач

The paper is devoted to the regularization of the classical optimality conditions (COC) — the Lagrange principle and the Pontryagin maximum principle in a convex optimal control problem for a parabolic equation with an operator (pointwise state) equality-constraint at the final time. The problem contains distributed, initial and boundary controls, and the set of its admissible controls is not assumed to be bounded. In the case of a specific form of the quadratic quality functional, it is natural to interpret the problem as the inverse problem of the final observation to find the perturbing effect that caused this observation. The main purpose of regularized COCs is stable generation of minimizing approximate solutions (MAS) in the sense of J. Warga. Regularized COCs are: 1) formulated as existence theorems of the MASs in the original problem with a simultaneous constructive representation of specific MASs; 2) expressed in terms of regular classical Lagrange and Hamilton–Pontryagin functions; 3) are sequential generalizations of the COCs and retain the general structure of the latter; 4) “overcome” the ill-posedness of the COCs, are regularizing algorithms for solving optimization problems, and form the theoretical basis for the stable solving modern meaningful ill-posed optimization and inverse problems.

К вопросу о регуляризации классических условий оптимальности в выпуклой задаче оптимального управления c фазовыми ограничениями

We consider the regularization of classical optimality conditions in a convex optimal control problem for a linear system of ordinary differential equations with pointwise state constraints such as equality and inequality, understood as constraints in the Hilbert space of square-integrable functions. The set of admissible task controls is traditionally embedded in the space of square-integrable functions. However, the target functional of the optimization problem is not, generally speaking, strongly convex. Obtaining regularized classical optimality conditions is based on a technique involving the use of two regularization parameters. One of them is used for the regularization of the dual problem, while the other is contained in a strongly convex regularizing addition to the target functional of the original problem. The main purpose of the obtained regularized Lagrange principle and the Pontryagin maximum principle is the stable generation of minimizing approximate solutions in the sense of J. Varga for the purpose of practical solving the considered optimal control problem with pointwise state constraints.

ON OPTIMALITY CONDITIONS FOR INTERVAL OPTIMIZATION PROBLEMS USING GENERALIZED HUKUHRA DIFFERENCE AND CONSTRAINED INTERVAL ARITHMETIC

In the presented paper we derive necessary optimality conditions for mathematical optimisation problems which involve both objective function and inequality constraints in interval form. In the proposed work both objective function and inequality constraints are derived from continuous function. For the formation of continuous function we used the idea of generalised Hukuhara difference (gH-difference), generalised Hukuhara derivative and constrained interval arithmetic. The objective of this paper is to derive new necessary optimality conditions for interval valued optimisation problem on the basis of classical optimality conditions given by Karush-Kuhn-Tucker (KKT). Finally, the proposed conditions are illustrated with the help of numerical examples.

Constructive generalization of classical sufficient second-order optimality conditions

In this paper, we consider new sufficient conditions of optimality of the second-order for equality constrained optimization problems, which essentially enhance and complement the classical ones and are constructive. For example, they establish equivalence between sufficient conditions in the equality constrained optimization problems and sufficient conditions for optimality in equality constrained problems by reducing the latter to equalities with the help of introducing slack variables. Previously, when using the classical sufficient optimality conditions, this fact was not considered to be true, that is, the existing classical sufficient conditions were not complete, so the proposed optimality conditions complement the classical ones and close the question of the equivalence of the problems with inequalities and the problems with equalities when reducing the first to the second by introducing slack variables.

ЗАЧЕМ НУЖНА РЕГУЛЯРИЗАЦИЯ ПРИНЦИПА ЛАГРАНЖА И ПРИНЦИПА МАКСИМУМА ПОНТРЯГИНА И ЧТО ОНА ДАЕТ

We consider the regularization of the classical Lagrange principle and the Pontryagin maximum principle in convex problems of mathematical programming and optimal control. On example of the “simplest” problems of constrained infinitedimensional optimization, two main questions are discussed: why is regularization of the classical optimality conditions necessary and what does it give?

Stable sequential Kuhn-Tucker theorem in iterative form or a regularized Uzawa algorithm in a regular nonlinear programming problem

A parametric nonlinear programming problem in a metric space with an operator equality constraint in a Hilbert space is studied assuming that its lower semicontinuous value function at a chosen individual parameter value has certain subdifferentiability properties in the sense of nonlinear (nonsmooth) analysis. Such subdifferentiability can be understood as the existence of a proximal subgradient or a Frechet subdifferential. In other words, an individual problem has a corresponding generalized Kuhn-Tucker vector. Under this assumption, a stable sequential Kuhn-Tucker theorem in nondifferential iterative form is proved and discussed in terms of minimizing sequences on the basis of the dual regularization method. This theorem provides necessary and sufficient conditions for the stable construction of a minimizing approximate solution in the sense of Warga in the considered problem, whose initial data can be approximately specified. A substantial difference of the proved theorem from its classical same-named analogue is that the former takes into account the possible instability of the problem in the case of perturbed initial data and, as a consequence, allows for the inherited instability of classical optimality conditions. This theorem can be treated as a regularized generalization of the classical Uzawa algorithm to nonlinear programming problems. Finally, the theorem is applied to the “simplest” nonlinear optimal control problem, namely, to a time-optimal control problem.

On necessary optimality conditions in discrete control systems

The paper deals with a nonlinear discrete-time optimal control problem with a cost functional of terminal type. Using a new variation of the control and new properties of optimal controls, we prove the linearised optimality conditions extending such classical optimality conditions. Along with this, various optimality conditions of quasi-singular controls are obtained. Finally, the examples illustrating the rich content of the obtained results are illustrated.

Kalman Duality Principle for a Class of Ill-Posed Minimax Control Problems with Linear Differential-Algebraic Constraints

In this paper we present Kalman duality principle for a class of linear Differential-Algebraic Equations (DAE) with arbitrary index and time-varying coefficients. We apply it to an ill-posed minimax control problem with DAE constraint and derive a corresponding dual control problem. It turns out that the dual problem is ill-posed as well and so classical optimality conditions are not applicable in the general case. We construct a minimizing sequence $\hat{u}_{\varepsilon}$ for the dual problem applying Tikhonov method. Finally we represent $\hat{u}_{\varepsilon}$ in the feedback form using Riccati equation on a subspace which corresponds to the differential part of the DAE.

Generalized convexity for non-regular optimization problems with conic constraints

In non-regular problems the classical optimality conditions are totally inapplicable. Meaningful results were obtained for problems with conic constraints by Izmailov and Solodov (SIAM J Control Optim 40(4):1280–1295, 2001). They are based on the so-called 2-regularity condition of the constraints at a feasible point. It is well known that generalized convexity notions play a very important role in optimization for establishing optimality conditions. In this paper we give the concept of Karush–Kuhn–Tucker point to rewrite the necessary optimality condition given in Izmailov and Solodov (SIAM J Control Optim 40(4):1280–1295, 2001) and the appropriate generalized convexity notions to show that the optimality condition is both necessary and sufficient to characterize optimal solutions set for non-regular problems with conic constraints. The results that exist in the literature up to now, even for the regular case, are particular instances of the ones presented here.

First and second order necessary optimality conditions for a discrete-time optimal control problem with a vector-valued objective function

The paper deals with a nonlinear discrete-time optimal control problem with a vector-valued objective function of terminal type. For the admissible controls of the problem under consideration which satisfy the additional regularity condition of the Lyusternik type we prove the first and second necessary optimality conditions extending such the classical optimality conditions as the Euler condition and the negativity of second variation of the objective function.

Classical necessary optimality conditions in discrete optimal control problems with nonlocal conditions

We consider an optimal control problem in which the system’s state is described by a system of difference equations with nonlocal (two-point) conditions; this problem includes, as particular cases, the initial value problem (Cauchy problem) and different types of boundary value problems. It is assumed that the admissible controls take values from an open set. The first and second functional variations are calculated; these variations are used to express first and second order necessary optimality conditions in the classical sense for discrete optimal control problems.

An application of matrix computations to classical second-order optimality conditions

For finite dimensional optimization problems with equality and inequality constraints, a weak constant rank condition (WCR) was introduced by Andreani–Martinez–Schuverdt (AMS) (Optimization 5–6:529–542, 2007) to study classical necessary second-order optimality conditions. However, this condition is not easy to check. Using a polynomial and matrix computation tools, we can substitute it by a weak constant rank condition (WCRQ) for an approximated problem and we present a method for checking points that satisfy WCRQ. We extend the result of (Andreani et al. in Optimization 5–6:529–542, 2007), we show that WCR can be replaced by WCRQ and we prove that these two conditions are independent.

Elementary proof of classical necessary optimality conditions for a mathematical programming problem with inequality constraints

Elementary proof of classical necessary optimality conditions for a mathematical programming problem with inequality constraints