Sufficient Optimality Conditions Research Articles

On multiobjective fractional programs with vanishing constraints

The aim of this article is to combine the study of fractional programming and mathematical programs with vanishing constraints for the first time in literature. This paper deals with multiobjective fractional programs with vanishing constraints (MFPVC) involving continuously differentiable functions. Necessary and sufficient optimality conditions are derived for a feasible point to be an efficient (or local efficient) solution of the (MFPVC). A parametric dual model has been formulated and duality results are established with the primal (MFPVC).

Constraint Qualifications and Optimality Conditions for Nonsmooth Semidefinite Multiobjective Programming Problems with Mixed Constraints Using Convexificators

In this article, we investigate a class of non-smooth semidefinite multiobjective programming problems with inequality and equality constraints (in short, NSMPP). We establish the convex separation theorem for the space of symmetric matrices. Employing the properties of the convexificators, we establish Fritz John (in short, FJ)-type necessary optimality conditions for NSMPP. Subsequently, we introduce a generalized version of Abadie constraint qualification (in short, NSMPP-ACQ) for the considered problem, NSMPP. Employing NSMPP-ACQ, we establish strong Karush-Kuhn-Tucker (in short, KKT)-type necessary optimality conditions for NSMPP. Moreover, we establish sufficient optimality conditions for NSMPP under generalized convexity assumptions. In addition to this, we introduce the generalized versions of various other constraint qualifications, namely Kuhn-Tucker constraint qualification (in short, NSMPP-KTCQ), Zangwill constraint qualification (in short, NSMPP-ZCQ), basic constraint qualification (in short, NSMPP-BCQ), and Mangasarian-Fromovitz constraint qualification (in short, NSMPP-MFCQ), for the considered problem NSMPP and derive the interrelationships among them. Several illustrative examples are furnished to demonstrate the significance of the established results.

Moreau-Yosida regularization to optimal control of the monodomain model with pointwise control and state constraints in cardiac electrophysiology

In this work, we study the optimal control problem of a coupled reaction-diffusion system, which is a monodomain model in cardiac electrophysiology with pointwise bilateral control and state constraints. We adopt the Moreau-Yosida regularization as a penalization technique to deal with the state constraints. The regularized problem’s first-order optimality condition is derived. In addition, sufficient second-order optimality condition is derived for the regularized problem using the virtual control concept by proving equivalence between Moreau-Yosida regularization and the virtual control concept. The convergence of optimal controls of the regularized problems to the optimal control of the original problem is proved. Moreover, the semi-smooth Newton method for numerically finding the optimal solution to the regularization problem is presented. Finally, numerical experiments are conducted, and the results allow us to understand the extinction of the wave excitation in cardiac defibrillation in the presence of both control and state constraints.

Solving Two-stage Quadratic Multiobjective Problems via Optimality and Relaxations

This paper focuses on the study of robust two-stage quadratic multiobjective optimization problems. We formulate new necessary and sufficient optimality conditions for a robust two-stage multiobjective optimization problem. The obtained optimality conditions are presented by means of linear matrix inequalities and thus they can be numerically validated by using a semidefinite programming problem. The proposed optimality conditions can be elaborated further as second-order conic expressions for robust two-stage quadratic multiobjective optimization problems with separable functions and ellipsoidal uncertainty sets. We also propose relaxation schemes for finding a (weak) efficient solution of the robust two-stage multiobjective problem by employing associated semidefinite programming or second-order cone programming relaxations. Moreover, numerical examples are given to demonstrate the solution variety of our flexible models and the numerical verifiability of the proposed schemes.

Optimality analysis on ℓ0/1 -sparsity regularized nonsmooth optimization

ABSTRACT We consider the problem of minimizing the sum of a nonsmooth function and an ℓ 0 / 1 -sparsity function, which is called the ℓ 0 / 1 -sparsity regularized nonsmooth optimization ( ℓ 0 / 1 -RNO). In this paper, by defining and analyzing two classes of stationary points, we present the first-order necessary and sufficient optimality conditions for ℓ 0 / 1 -RNO. Moreover, applying these optimality results on ℓ 0 / 1 -RNO, we derive the first-order necessary and sufficient optimality conditions for both the nonsmooth M-estimation of linear regression with positive variable sparsity, and ℓ 1 -penalty minimum of 0/1 composite optimization. Especially, we obtain that the given ℓ 1 -penalty is strongly exact penalty of 0/1 composite optimization.

Linear quadratic optimal control of stochastic 2-D Roesser models

This paper investigates the linear quadratic (LQ) optimal control problem for the stochastic two-dimensional (2-D) systems governed by Roesser models with multiplicative noise. The main contribution is to give the necessary and sufficient optimality condition by proposing a set of novel forward and backward stochastic partial difference equations (FBSPDE), and to further present the explicitly optimal feedback control laws on the finite horizon and on the infinite horizon based on the Riccati-like difference equations and the algebraic equation, respectively. Several numerical simulations are provided to illustrate the performance of the designed controllers.

Robust Recovery of Optimally Smoothed Polymer Relaxation Spectrum from Stress Relaxation Test Measurements.

The relaxation spectrum is a fundamental viscoelastic characteristic from which other material functions used to describe the rheological properties of polymers can be determined. The spectrum is recovered from relaxation stress or oscillatory shear data. Since the problem of the relaxation spectrum identification is ill-posed, in the known methods, different mechanisms are built-in to obtain a smooth enough and noise-robust relaxation spectrum model. The regularization of the original problem and/or limit of the set of admissible solutions are the most commonly used remedies. Here, the problem of determining an optimally smoothed continuous relaxation time spectrum is directly stated and solved for the first time, assuming that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. The relaxation time spectrum model that reproduces the relaxation modulus measurements and is the best smoothed in the class of continuous square-integrable functions is sought. Based on the Hilbert projection theorem, the best-smoothed relaxation spectrum model is found to be described by a finite sum of specific exponential-hyperbolic basis functions. For noise-corrupted measurements, a quadratic with respect to the Lagrange multipliers term is introduced into the Lagrangian functional of the model's best smoothing problem. As a result, a small model error of the relaxation modulus model is obtained, which increases the model's robustness. The necessary and sufficient optimality conditions are derived whose unique solution yields a direct analytical formula of the best-smoothed relaxation spectrum model. The related model of the relaxation modulus is given. A computational identification algorithm using the singular value decomposition is presented, which can be easily implemented in any computing environment. The approximation error, model smoothness, noise robustness, and identifiability of the polymer real spectrum are studied analytically. It is demonstrated by numerical studies that the algorithm proposed can be successfully applied for the identification of one- and two-mode Gaussian-like relaxation spectra. The applicability of this approach to determining the Baumgaertel, Schausberger, and Winter spectrum is also examined, and it is shown that it is well approximated for higher frequencies and, in particular, in the neighborhood of the local maximum. However, the comparison of the asymptotic properties of the best-smoothed spectrum model and the BSW model a priori excludes a good approximation of the spectrum in the close neighborhood of zero-relaxation time.

A Minimax-Program-Based Approach for Robust Fractional Multi-Objective Optimization

In this paper, by making use of some advanced tools from variational analysis and generalized differentiation, we establish necessary optimality conditions for a class of robust fractional minimax programming problems. Sufficient optimality conditions for the considered problem are also obtained by means of generalized convex functions. Additionally, we formulate a dual problem to the primal one and examine duality relations between them. In our results, by using the obtained results, we obtain necessary and sufficient optimality conditions for a class of robust fractional multi-objective optimization problems.

Sufficient Optimality Conditions for Hybrid Systems of Variable Dimension with Intermediate Constraints

Sufficient Optimality Conditions for Hybrid Systems of Variable Dimension with Intermediate Constraints

Leveraged Matrix Completion With Noise.

Completing low-rank matrices from subsampled measurements has received much attention in the past decade. Existing works indicate that O(nrlog2(n)) datums are required to theoretically secure the completion of an n ×n noisy matrix of rank r with high probability, under some quite restrictive assumptions: 1) the underlying matrix must be incoherent and 2) observations follow the uniform distribution. The restrictiveness is partially due to ignoring the roles of the leverage score and the oracle information of each element. In this article, we employ the leverage scores to characterize the importance of each element and significantly relax assumptions to: 1) not any other structure assumptions are imposed on the underlying low-rank matrix and 2) elements being observed are appropriately dependent on their importance via the leverage score. Under these assumptions, instead of uniform sampling, we devise an ununiform/biased sampling procedure that can reveal the "importance" of each observed element. Our proofs are supported by a novel approach that phrases sufficient optimality conditions based on the Golfing scheme, which would be of independent interest to the wider areas. Theoretical findings show that we can provably recover an unknown n×n matrix of rank r from just about O(nrlog2 (n)) entries, even when the observed entries are corrupted with a small amount of noisy information. The empirical results align precisely with our theories.

Optimality Conditions for Mathematical Programs with Vanishing Constraints Using Directional Convexificators

This article deals with mathematical programs with vanishing constraints (MPVCs) involving lower semi-continuous functions. We introduce generalized Abadie constraint qualification (ACQ) and MPVC-ACQ in terms of directional convexificators and derive necessary KKT-type optimality conditions. We also derive sufficient conditions for global optimality for the MPVC under convexity utilizing directional convexificators. Further, we introduce a Wolfe-type dual model in terms of directional convexificators and derive duality results. The results are well illustrated by examples.

Optimality and Unified Duality for E-differentiable Vector Optimization Problems over Cones involving Generalized E-convexity

This paper explores a new approach to solve a nondifferentiable vector optimization problem over cones by means of an operator E : Rn → Rn which renders differentiability to the considered problem. Some new generalized convexity notions are introduced and employed to obtain necessary and sufficient KKT-type optimality conditions. Further, a unified dual is associated with the considered problem encapsulating both Mond-Weir and Wolfe type duals in the same apparatus and duality results are proved.

Second-order necessary and sufficient optimality conditions for multiobjective interval-valued nonlinear programming

In this article, the second-order optimality conditions for nonlinear programming with multiple interval-valued objective functions (in brief, NPMIOF) are studied. In the first part, the second- and first-order necessary optimality conditions for some types of efficient solutions of NPMIOF are established under of Abadie second- and first-order constraint qualifications. In the next part, we investigate both primal and dual second-order sufficient optimality conditions. Our second-order necessary conditions enhance the previous results. The second-order sufficient optimality conditions are new.

An extension of Pontryagin Maximum principle in interval environment and its application to inventory problem

The control theory is one of the most fundamental branches of engineering as it implicates solving abilities of numerous non-linear engineering design problems efficiently. Again, Pontryagin’s maximum principle is one of the most salient topics of control theory as it is involved in solving various important problems. However, in the current highly complex situation, most of such real-life problems appear to be highly uncertain/imprecise in nature. Consequently, in order to analyse such problems accurately, uncertainty/flexibility of such problems cannot be overestimated. Motivating from this fact and as a necessity, in this study, the Pontryagin’s maximum principles are extended in interval environment for interval valued control problems (IVCPs). In this context, an IVCP is defined along with different formal terminologies. Further, the necessary and sufficient optimality conditions (i.e., Pontryagin’s maximum principles) are extended for IVCPs using existing interval ranking proposed by Bhunia and Samanta (2014). Further, in the second part of this work, in order to test the effectiveness of the proposed theories, an economic order quantity (EOQ) model is developed by considering dynamic servicing strategies in interval environment. With the help of numerical example, the proposed extension of Pontryagin’s maximum principles for IVCPs is well validated.

Optimality conditions for differentiable linearly constrained pseudoconvex programs

AbstractThe aim of this paper is to study optimality conditions for differentiable linearly constrained pseudoconvex programs. The stated results are based on new transversality conditions which can be used instead of complementarity ones. Necessary and sufficient optimality conditions are stated under suitable generalized convexity properties. Moreover, two different pairs of dual problems are proposed and weak and strong duality results proved. Finally, it is shown how transversality conditions can be applied to characterize optimality of convex quadratic problems and to efficiently solve a particular class of Max-Min problems

Optimal quadratic full state feedback control of nonlinear affine systems

The solution of the optimal quadratic full-state feedback closed-loop control of a nonlinear affine system problem is presented and solved. This solution uses the State-Dependent Coefficient (SDC) form of a nonlinear system and the Calculus of Variations. Further, it is shown that this solution enables the computation of the value function associated with the respective Hamilton-Jacobi-Bellman equation thus satisfying the necessary and sufficient conditions for optimality. The use of the SDC form representation in the optimal solution for a nonlinear affine system results in an explicit and exact full-state feedback closed-loop control implementation as opposed to the existing open-loop solutions of the optimal control. This solution, like for the linear case, is non-causal and thus cannot be solved in real-time. The closed-loop application involves precomputing a time-varying full-state feedback matrix. The optimal feedback is a linear combination of the current state. The time-varying gain matrix is the backward solution of a non-symmetric matrix Riccati equation. Conditions for the stability of the full-state feedback are presented. This new representation of the optimal solution gives a well-understandable algorithm that enables implementation in the closed loop of the optimal feedback control of a non-linear system which is not possible with the existing optimal open loop representations.

Optimal distributed control for a Cahn-Hilliard-Darcy system with mass sources, unmatched viscosities and singular potential

We study a Cahn-Hilliard-Darcy system with mass sources, which can be considered as a basic, though simplified, diffuse interface model for the evolution of tumor growth. This system is equipped with an impermeability condition for the averaged velocity u as well as homogeneous Neumann boundary conditions for the phase function ϕ and the chemical potential µ. The source term in the convective Cahn-Hilliard equation contains a control R that can be thought as a drug or a nutrient in applications. Our goal is to study a distributed optimal control problem in the two-dimensional setting with a cost functional of tracking-type. In the physically relevant case with unmatched viscosities for the binary fluid mixtures and a singular potential, we first prove the existence and uniqueness of a global strong solution with ϕ being strictly separated from the pure phases ±1. This well-posedness result enables us to characterize the control-to-state mapping S: R→ϕ. Then we obtain the existence of an optimal control, the Frechet differentiability of S and first-order necessary optimality conditions expressed through a suitable variational inequality for the adjoint variables. Finally, we prove the differentiability of the control-to-costate operator and establish a second-order sufficient condition for the strict local optimality.

The Optimality Conditions for Generalized Uniform η – V – I Invariant Convexity Programming

Generalized uniformly invariant convex programming is an important optimization problem that has numerous practical applications, such as transportation planning, engineering optimization, etc. The study of its optimal conditions can help solve practical problems and improve efficiency. Optimality conditions serve as the foundation of optimization theory by aiding in understanding the nature and characteristics of problems and providing guidance for the design and analysis of optimization algorithms. Therefore, studying the optimality conditions of generalized uniformly invariant convex programming is highly significant for solving practical problems, promoting the development of optimization theory and algorithm design and analysis, as well as fostering interdisciplinary research. In this research, a novel type of generalized uniform η-V-I invexity function is defined through the use of the η - subdifferential. In the case of the new generalized convex functions, a certain of multiobjective programming with this generalized convexity is discussed, and the corresponding sufficient optimality conditions are obtained.

Numerical analysis of a nonsmooth quasilinear elliptic control problem: I. Explicit second-order optimality conditions

In this paper, we derive explicit second-order necessary and sufficient optimality conditions of a local minimizer to an optimal control problem for a quasilinear second-order partial differential equation with a piecewise smooth but not differentiable nonlinearity in the leading term. The key argument rests on the analysis of level sets of the state. Specifically, we show that if a function vanishes on the boundary and its the gradient is different from zero on a level set, then this set decomposes into finitely many closed simple curves. Moreover, the level sets depend continuously on the functions defining these sets. We also prove the continuity of the integrals on the level sets. In particular, Green’s first identity is shown to be applicable on an open set determined by two functions with nonvanishing gradients. In the second part to this paper, the explicit sufficient second-order conditions will be used to derive error estimates for a finite-element discretization of the control problem.

Higher-order KKT optimality conditions through contingent derivatives for constrained nonsmooth vector equilibrium problems

In this paper, we deal with some higher-order optimality conditions for local strict efficient solutions to a nonsmooth vector equilibrium problem with set, cone and equality constraints. For this aim, the concept of m−stable and m−steady functions (m≥2 and integer) for single-valued functions and some constraint qualifications of higher order in terms of contingent derivatives are proposed accordingly. We analyze the sum calculus rule of mth-order adjacent set, mth-order interior set, asymptotic mth-order tangent cone and asymptotic mth-order adjacent cone. Subsequently, we employ the obtained calculus rules to treat KKT necessary and sufficient optimality conditions of higher order in terms of contingent derivatives for the mth-order (local) strict efficient solutions to such problem. Simultaneously, we employ these rules to study KKT higher-order optimality conditions for such efficient solutions for a nonsmooth vector optimization problem with constraints. Some illustrative examples are provided to demonstrate the main results of the literature.