Sufficient Optimality Conditions Research Articles

Robust estimation of state vector coordinates in the controlled helicopter motion problem

The problem of finding H∞ – a observer of the state vector of a linear continuous non-stationary dynamical system with a semi-infinite time of functioning is considered. It is assumed that a mathematical model of a closed-loop linear continuous deterministic dynamical system with an optimal linear regulator, found as a result of minimization of the quadratic quality criterion, is known. For solving the state observer synthesis problem the reduction of the problem to a min-max optimal control problem is used. In this problem, the minimum of the quality criterion is sought by the observer’s gain matrix, and the maximum – by the external influence, measurement noise, and initial conditions. To solve this problem, the extension principle is applied and sufficient optimality conditions are obtained that requires the choice of auxiliary functions of the Krotov–Bellman type. As a result of the implementation of the procedure for choosing an auxiliary function and using the rules of matrix differentiation, relations for the synthesis of the observer and formulas for finding the best matrix of observer gains, as well as the laws for choosing the worst external influences and noise, were obtained. We find a solution to the problem of state vector coordinates estimation in the presence of limited external influences and disturbances in a linear model of the measuring system. As an example, the equations of motion of the Raptor-type helicopter are used.

Sparse optimal control of a quasilinear elliptic PDE in measure spaces

<p style='text-indent:20px;'>We prove existence of optimal controls for sparse optimal control of a quasilinear elliptic equation in measure spaces and derive first-order necessary optimality conditions. Under additional assumptions also second-order necessary and sufficient optimality conditions are obtained.</p>

Stochastic maximum principle for weighted mean-field system

We study the optimal control problem for a weighted mean-field system. A new feature of the control problem is that the coefficients depend on the state process as well as its weighted measure and the control variable. By applying variational technique, we establish a stochastic maximum principle. Also, we establish a sufficient condition of optimality. As an application, we investigate the optimal premium policy of an insurance firm for asset–liability management problem.

The warehouse reshuffling problem with swap moves and time limit

In this paper, we study the warehouse reshuffling problem, where pallets in a storage have to be rearranged in an efficient way. A high-bay warehouse with an automated storage and retrieval system is considered, which is equipped with a twin shuttle stacker crane. This twin shuttle is designed to perform swap moves, where the pallet at a storage location is swapped with the pallet currently loaded on the stacker crane. We study a new problem variant, where the time for reshuffling is limited, and the desired assignment of pallets to storage locations is not given as input. The objective is to store the frequently accessed pallets closely to the input/output-point to keep the warehouse operations efficient. After proposing necessary and sufficient optimality conditions for assignments of pallets to storage locations, we present algorithms to deal with the time limit. Moreover, we prove NP-hardness of two special cases, introduce lower bound procedures, several construction heuristics and a simulated annealing algorithm. Finally, we present computational results on randomly generated instances based on a real-world company setting.

A sufficient maximum principle for backward stochastic systems with mixed delays.

In this paper, we study the problem of optimal control of backward stochastic differential equations with three delays (discrete delay, moving-average delay and noisy memory). We establish the sufficient optimality condition for the stochastic system. We introduce two kinds of time-advanced stochastic differential equations as the adjoint equations, which involve the partial derivatives of the function $ f $ and its Malliavin derivatives. We also show that these two kinds of adjoint equations are equivalent. Finally, as applications, we discuss a linear-quadratic backward stochastic system and give an explicit optimal control. In particular, the stochastic differential equations with time delay are simulated by means of discretization techniques, and the effect of time delay on the optimal control result is explained.

Necessary and sufficient optimality conditions for fractional interval-valued variational problems

In this paper a special kind of variational programming problem involving fractional interval-valued objective function is considered. For such type of problem, insights into LU optimal solutions have been discussed. Using the LU optimal concept, we established optimality conditions for the considered problem. Further, We formulated a Mond-Weir dual problem and discussed appropriate duality theorems for the relationship between dual and primal problems.

On nondifferentiable semi-infinite multiobjective programming with interval-valued functions

In the paper, we investigate a class of nondifferentiable semi-infinite multiobjective programming problems such that all functions constituting them are interval-valued. We derive both Fritz John necessary optimality conditions and, under a constraint qualification, Karush-Kuhn-Tucker necessary optimality conditions for (weak) $ LU $-Pareto solutions in the considered nondifferentiable semi-infinite vector interval-valued optimization problem. Under appropriate invexity hypotheses, we also prove sufficient optimality conditions for this semi-infinite interval-valued vector optimization problem. Further, we also use the $ l_{1} $ exact function method for solving the aforesaid nondifferentiable interval-valued multicriteria optimization problem. Then, we analyze the property of exactness of the penalization for the absolute value exact penalty function method under assumption that the functions involved in the considered semi-infinite multiobjective programming problem are locally Lipschitz interval-valued invex functions. The conditions guaranteeing the equivalence of the sets of (weak) $ LU $-Pareto solutions in the original semi-infinite interval-valued multiobjective programming problem and its associated vector penalized optimization problem with the multiple interval-valued $ l_{1} $ exact penalty function are given.

THE OPTIMAL CONTROL PROBLEM FOR SYSTEMS OF INTEGRO-DIFFERENTIAL EQUATIONS ON THE HALF-AXIS

This article is devoted to exploring the optimal control problem for a system of integro-differential equations on the infinite interval. Sufficient conditions for the existence of optimal controls and trajectories have been obtained in terms of right-hand sides and the quality criterion function. Integro-differential equation systems are the mathematical models for many natural science processes, such as those in fluid dynamics and kinetic chemistry, among others. Many of these equations have the control that minimizing specific functionals related to the dynamics of these processes. This work specifically focuses on deriving sufficient optimality conditions for integro-differential systems on the half-axis. The complexity of the research is in the following aspects: Firstly, the problem at hand involves optimal control with an infinite horizon, which makes the direct application of compactness criteria like the Arzela-Ascoli theorem impossible. Secondly, the problem is considered up to the moment $\tau$ when the solution reaches the boundary of the domain. This reach moment depends on the control $\tau = \tau(u)$. Hence, the solution to the problem is essentially represented by the triplet $(u^*, x^*, \tau^*)$ — the optimal control, the optimal trajectory, and the optimal exit time. We note that a particular case of this problem is the problem of optimal speed. The main idea of proving the existence of an optimal solution relies on a compactness approach and involves the following steps: identifying a weakly convergent minimizing sequence of admissible controls, extracting a strongly convergent subsequence of corresponding trajectories, and justifying boundary transitions in equations and the quality criterion. The work provides a problem statement, formulates, and proves the main result.

Approximate optimality conditions and mixed type duality for semi-infinite multiobjective programming problems involving tangential subdifferentials

The aim of this paper is to study the approximate solution for semi-infinite multiobjective programming problems related to tangential subdifferentials. Under some mild assumptions, we obtain approximate necessary and sufficient optimality conditions of a (weakly) quasi $ \varepsilon $-efficient solution for semi-infinite multiobjective programming problems. We also introduce a mixed type dual model to the semi-infinite multiobjective programming problem, which is different from the one considered in the literature. Finally, we derive some approximate weak, strong and converse duality theorems under the assumptions of generalized convexity. Our results extend and improve the corresponding results in the literature. Some examples are given to illustrate some advantages of our results.

Stochastic optimal control for dynamics of forward backward doubly SDEs of mean-field type

In this paper we establish in first the existence of strong optimal solutions of a control problem for dynamics driven by a linear forward-backward doubly stochastic differential equations of mean-field type (MF-FBDSDEs), with random coefficients and non linear functional cost. Moreover, we establish necessary as well as sufficient optimality conditions for this kind of control problem. In the second part of this paper, we establish necessary as well as sufficient optimality conditions for existence of both optimal relaxed control and optimal strict control for dynamics of nonlinear mean-field forward-backward doubly stochastic differential equations.

Extremal Problems for Infinite Order Parabolic Systems with Boundary Conditions Involving Integral Time Lags

Extremal problems for integral time lag infinite order parabolic systems are studied in the paper. An optimal boundary control problem for distributed infinite order parabolic systems in which integral time lags appear in the Neumann boundary conditions is solved. Such equations constitute in a linear approximation a universal mathematical model for many diffusion processes (e.g., modeling and control of heat transfer processes). The time horizon is fixed. Using the Dubovicki-Milutin framework, the necessary and sufficient conditions of optimality for the Neumann problem with the quadratic performance indexes and constrained control are derived.

A sufficient condition for optimal control problem of fully coupled forward‐backward stochastic systems with jumps: A state‐constrained control approach

AbstractWe study the stochastic optimal control problem for fully coupled forward‐backward stochastic differential equations (FBSDEs) with jump diffusions. A major technical challenge of such problems arises from the dependence of the (forward) diffusion term on the backward SDE and the presence of jump diffusions. Previously, this class of problems has been solved via only the stochastic maximum principle, which guarantees only the necessary condition of optimality and requires identifying unknown parameters in the corresponding variational inequality. Our paper provides an alternative approach, which constitutes the sufficient condition for optimality. Specifically, the original fully coupled FBSDE control problem (referred to as (P)) is converted into the terminal state‐constrained forward stochastic control problem (referred to as ) that includes additional (possibly unbounded) control variables. Then is solved via the backward reachability analysis, by which the value function of is expressed as the zero‐level set of the value function for the auxiliary unconstrained (forward) control problem (referred to as ). Unlike ), is an unconstrained problem, which includes additional control variables as a consequence of the martingale representation theorem. We show that the value function for is the unique viscosity solution to the associated integro‐type Hamilton‐Jacobi‐Bellman (HJB) equation. The viscosity solution analysis presented in our paper requires a new technique due to additional control variables in the Hamiltonian maximization and the presence of the nonlocal integral operator in terms of the (singular) Lévy measure. To solve the original problem (P), we reverse our approach. Specifically, we first solve to obtain the value function using the verification theorem and the viscosity solution of the HJB equation. Then is solved by characterizing the zero‐level set of the value function of , from which the optimal solution of (P) can be constructed. To illustrate the theoretical results of this paper, applications to the linear‐quadratic problem for fully coupled FBSDEs with jumps are also presented.

Dynamic Programming and a Verification Theorem for the Recursive Stochastic Control Problem of Jump-Diffusion Models With Random Coefficients

In this article, we consider the stochastic optimal control problem for (forward) stochastic differential equations (SDEs) with jump diffusions and random coefficients under a recursive-type objective functional captured by a backward SDE (BSDE). Due to the jump-diffusion process with random coefficients in both the constraint (forward SDE) and the recursive BSDE objective functional, the associated Hamilton–Jacobi–Bellman equation (HJBE) is an integro-type second-order nonlinear stochastic PDE driven by both Brownian motion and (compensated) Poisson process, which we call the integro-type stochastic HJBE (ISHJBE) with jump diffusions. We first prove the dynamic programming principle for the value function using the backward semigroup associated with the recursive objective functional and the precise estimates of BSDEs, by which the continuity of the value function is also shown. Then we establish a verification theorem, which provides a sufficient condition of optimality and characterizes the value function using the (stochastic) solution of the ISHJBE with jump diffusions. Under suitable assumptions, we show the existence and uniqueness of the weak solution to the ISHJBE via the Sobolev space technique. Finally, we apply the verification theorem to the general indefinite linear-quadratic problem and the utility maximization problem; for both problems, explicit optimal solutions are characterized by solving the corresponding ISHJBE with jump diffusions.

Optimality conditions in set-valued optimization using approximations as generalized derivatives

Abstract The set criterion is an appropriate defining approach regarding the solutions for the set-valued optimization problems. By using approximations as generalized derivatives of set-valued mappings, we establish necessary optimality conditions for a constrained set-valued optimization problem in the sense of set optimization in terms of asymptotical pointwise compact approximations. Sufficient optimality conditions are then obtained through first-order strong approximations of data set-valued mappings.

Approximate-Optimal Synthesis of Operational Control Systems for Dynamic Objects on the Basis of Quasilinearization and Sufficient Optimality Conditions

Approximate-Optimal Synthesis of Operational Control Systems for Dynamic Objects on the Basis of Quasilinearization and Sufficient Optimality Conditions

On duality in convex optimization of second-order differential inclusions with periodic boundary conditions

The present paper is devoted to the duality theory for the convex optimal control problem of second-order differential inclusions with periodic boundary conditions. First, we use an auxiliary problem with second-order discrete-approximate inclusions and focus on formulating sufficient conditions of optimality for the differential problem. Then, we concentrate on the duality that exists in periodic boundary conditions to establish a dual problem for the differential problem and prove that Euler-Lagrange inclusions are duality relations for both primal and dual problems. Finally, we consider an example of the duality for the second-order linear optimal control problem.

Quasi Efficient Solutions and Duality Results in a Multiobjective Optimization Problem with Mixed Constraints via Tangential Subdifferentials

We take up a nonsmooth multiobjective optimization problem with tangentially convex objective and constraint functions. In employing a suitable constraint qualification, we formulate both necessary and sufficient optimality conditions for (local) quasi efficient solutions in terms of tangential subdifferentials. Furthermore, under generalized convexity assumptions, we state strong, weak and converse duality relations of Wolfe and Mond–Weir types. We give a number of examples to illustrate the new concepts and main results of this paper.

Optimality Conditions and Dualities for Robust Efficient Solutions of Uncertain Set-Valued Optimization with Set-Order Relations

In this paper, we introduce a second-order strong subdifferential of set-valued maps, and discuss some properties, such as convexity, sum rule and so on. By the new subdifferential and its properties, we establish a necessary and sufficient optimality condition of set-based robust efficient solutions for the uncertain set-valued optimization problem. We also introduce a Wolfe type dual problem of the uncertain set-valued optimization problem. Finally, we establish the robust weak duality theorem and the robust strong duality theorem between the uncertain set-valued optimization problem and its robust dual problem. Several main results extend to the corresponding ones in the literature.

Duality of optimization problems with gauge functions

ABSTRACT Recently, Yamanaka and Yamashita proposed the so-called positively homogeneous optimization problem, which includes many important problems, such as the absolute-value and the gauge optimization problems. They presented a closed form of the dual formulation for the problem, and showed weak duality and the equivalence to the Lagrangian dual under some conditions. In this work, we focus on a special positively homogeneous optimization problem, whose objective function and constraints consist of some gauge and linear functions. We prove not only weak duality but also strong duality. We also study necessary and sufficient optimality conditions associated to the problem. Moreover, we give sufficient conditions under which we can recover a primal solution from a Karush-Kuhn-Tucker point of the dual formulation. Finally, we discuss how to extend the above results to general optimization problems by considering the so-called perspective functions.

Optimality conditions for MPECs in terms of directional upper convexifactors

In this paper, we investigate necessary and sufficient optimality conditions for mathematical programs with equilibrium constraints. For this goal, we introduce an appropriate type of MPEC regularity condition and a stationary concept given in terms of directional upper convexificators and directional upper semi-regular convexificators. The appearing functions are not necessarily smooth/locally Lipschitz/convex/continuous, and the continuity directions’ sets are not assumed to be compact or convex. Finally, notions of directional pseudoconvexity and directional quasiconvexity are used to establish sufficient optimality conditions for MPECs.