Resolvents and Yosida Approximations of Displacement Mappings of Isometries

Set-Valued and Variational Analysis - Maximally monotone operators are fundamental objects in modern optimization. The main classes of monotone operators are subdifferential operators and matrices...     

Conservative and Semismooth Derivatives are Equivalent for Semialgebraic Maps

Set-Valued and Variational Analysis - Subgradient and Newton algorithms for nonsmooth optimization require generalized derivatives to satisfy subtle approximation properties: conservativity for the...     

Analysis of Optimization Algorithms via Sum-of-Squares

Journal of Optimization Theory and Applications - We introduce a new framework for unifying and systematizing the performance analysis of first-order black-box optimization algorithms for...     

Algebraic Approach to Duality in Optimization and Applications

Set-Valued and Variational Analysis - This paper studies duality of optimization problems in a vector space without topological structure. A strong duality relation is established by means of...     

Set-Convergence and Its Application: A Tutorial

Set-Valued and Variational Analysis - Optimization problems, generalized equations, and a multitude of other variational problems invariably lead to the analysis of sets and set-valued mappings as...     

Primal and dual algorithms for optimization over the efficient set

ABSTRACT

Optimization over the efficient set of a multi-objective optimization problem is a mathematical model for the problem of selecting a most preferred solution that arises in multiple criteria decision-making to account for trade-offs between objectives within the set of efficient solutions. In this paper, we consider a particular case of this problem, namely that of optimizing a linear function over the image of the efficient set in objective space of a convex multi-objective optimization problem. We present both primal and dual algorithms for this task. The algorithms are based on recent algorithms for solving convex multi-objective optimization problems in objective space with suitable modifications to exploit specific properties of the problem of optimization over the efficient set. We first present the algorithms for the case that the underlying problem is a multi-objective linear programme. We then extend them to be able to solve problems with an underlying convex multi-objective optimization problem. We compare the new algorithms with several state of the art algorithms from the literature on a set of randomly generated instances to demonstrate that they are considerably faster than the competitors.     

Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization

Interpolation-based trust-region methods are an important class of algorithms for Derivative-Free Optimization which rely on locally approximating an objective function by quadratic polynomial interpolation models, frequently built from less points than there are basis components. Often, in practical applications, the contribution of the problem variables to the objective function is such that many pairwise correlations between variables are negligible, implying, in the smooth case, a sparse structure in the Hessian matrix. To be able to exploit Hessian sparsity, existing optimization approaches require the knowledge of the sparsity structure. The goal of this paper is to develop and analyze a method where the sparse models are constructed automatically. The sparse recovery theory developed recently in the field of compressed sensing characterizes conditions under which a sparse vector can be accurately recovered from few random measurements. Such a recovery is achieved by minimizing the ℓ ₁-norm of a vector subject to the measurements constraints. We suggest an approach for building sparse quadratic polynomial interpolation models by minimizing the ℓ ₁-norm of the entries of the model Hessian subject to the interpolation conditions. We show that this procedure recovers accurate models when the function Hessian is sparse, using relatively few randomly selected sample points. Motivated by this result, we developed a practical interpolation-based trust-region method using deterministic sample sets and minimum ℓ ₁-norm quadratic models. Our computational results show that the new approach exhibits a promising numerical performance both in the general case and in the sparse one.     

Fuzzy optimality conditions for fractional multi-objective problems

In this article, we are concerned with fractional multi-objective optimization problems. Since those problems are in general nonconvex problems even if the problem data are convex, using techniques from variational analysis especially the approximate extremal principle [B.S. Mordukhovich, Variational Analysis and Generalized Differentiation, I: Basic Theory, Grundlehren Series: Fundamental Principles of Mathematical Sciences, Vol. 330, Springer, Berlin, 2006; B.S. Mordukhovich, Variational Analysis and Generalized Differentiation, II: Applications, Grundlehren Series: Fundamental Principles of Mathematical Sciences, Vol. 331, Springer, Berlin, 2006], we develop fuzzy optimality conditions.     

Nonsmoothness in Machine Learning: Specific Structure,Proximal Identification,and Applications

Set-Valued and Variational Analysis - Nonsmoothness is often a curse for optimization; but it is sometimes a blessing, in particular for applications in machine learning. In this paper, we present...     

An Inertial Algorithm for DC Programming

Set-Valued and Variational Analysis - We consider nonsmooth optimization problems whose objective function is defined by the Difference of Convex (DC) functions. With the aim of computing critical...     

Frank–Wolfe and friends: a journey into projection-free first-order optimization methods

Invented some 65 years ago in a seminal paper by Marguerite Straus-Frank and Philip Wolfe, the Frank–Wolfe method recently enjoys a remarkable revival, fuelled by the need of fast and reliable first-order optimization methods in Data Science and other relevant application areas. This review tries to explain the success of this approach by illustrating versatility and applicability in a wide range of contexts, combined with an account on recent progress in variants, improving on both the speed and efficiency of this surprisingly simple principle of first-order optimization.

     

On the Stability of the Directional Regularity

In this paper we select two tools of investigation of the classical metric regularity of set-valued mappings, namely the Ioffe criterion and the Ekeland Variational Principle, which we adapt to the study of the directional setting. In this way, we obtain in a unitary manner new necessary and/or sufficient conditions for directional metric regularity. As an application, we establish stability of this property at composition and sum of set-valued mappings. In this process, we introduce directional tangent cones and the associated generalized primal differentiation objects and concepts. Moreover, we underline several links between our main assertions by providing alternative proofs for several results.

     

基于乌鸦喝水的元启发式优化算法

启发式优化算法已成为求解复杂优化问题的一种有效方法,可用于解决传统的优化方法难以求解的问题.受乌鸦喝水寓言故事启发,提出一种新型元启发式优化算法—乌鸦喝水算法,首先建立了乌鸦喝水算法数学模型;其次,给出实现该算法的详细步骤;最后,将该算法用于基准函数优化,并将该算法与乌鸦搜索算法、粒子群优化算法、多元宇宙优化算法、花授...     

Differential stability properties in convex scalar and vector optimization

Set-Valued and Variational Analysis - This paper focuses on formulas for the ε-subdifferential of the optimal value function of scalar and vector convex optimization problems. These formulas...     

Clustering approaches for dealing with multiple DNA microarray datasets

This paper centres on clustering approaches that deal with multiple DNA microarray datasets. Four clustering algorithms for deriving a clustering solution from multiple gene expression matrices studying the same biological phenomenon are considered: two unsupervised cluster techniques based on information integration, a hybrid consensus clustering method combining Particle Swarm Optimization and k-means that can be referred to supervised clustering, and a supervised consensus clustering algorithm enhanced by Formal Concept Analysis (FCA), which initially produces a list of different clustering solutions, one per each experiment and then these solutions are transformed by portioning the cluster centres into a single overlapping partition, which is further analyzed by employing FCA. The four algorithms are evaluated on gene expression time series obtained from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.     

First-order strong variation algorithms for optimal control

Journal of Optimization Theory and Applications - This paper presents two demonstrably convergent, first-order, differential dynamic programming algorithms for the solution of optimal control...     

A Steepest Descent Method for Set Optimization Problems with Set-Valued Mappings of Finite Cardinality

Journal of Optimization Theory and Applications - In this paper, we study a first-order solution method for a particular class of set optimization problems where the solution concept is given by...     

Exact calculus of Fréchet subdifferentials for Hadamard directionally differentiable functions

We continue the study of the calculus of the generalized subdifferentials started in [V.F. Demyanov, V. Roshchina, Exhausters and subdifferentials in nonsmooth analysis, Optimization (2006) (in press)] and [V. Roshchina, Relationships between upper exhausters and the basic subdifferential in Variational Analysis, Journal of Mathematical Analysis and Applications 334 (2007) 261–272] and provide some basic calculus rules for the Fréchet subdifferentials via collections of compact convex sets associated with Hadamard directional derivative. The main result of this paper is the sum rule for the Fréchet subdifferential in the form of an equality, which holds for Hadamard directionally differentiable functions, and is of significant interest from the points of view of both theory and applications.     

Two basic problems in reliability-based structural optimization

Optimization of structures with respect to performance, weight or cost is a well-known application of mathematical optimization theory. However optimization of structures with respect to weight or cost under probabilistic reliability constraints or optimization with respect to reliability under cost/weight constraints has been subject of only very few studies. The difficulty in using probabilistic constraints or reliability targets lies in the fact that modern reliability methods themselves are formulated as a problem of optimization. In this paper two special formulations based on the so-called first-order reliability method (FORM) are presented. It is demonstrated that both problems can be solved by a one-level optimization problem, at least for problems in which structural failure is characterized by a single failure criterion. Three examples demonstrate the algorithm indicating that the proposed formulations are comparable in numerical effort with an approach based on semi-infinite programming but are definitely superior to a two-level formulation.