Fixed‐order H∞ control design via a partially augmented Lagrangian method

In this paper we develop an augmented Lagrangian method to determine local optimal solutions of the reduced‐ and fixed‐order H_∞ synthesis problems. We cast these synthesis problems as optimization programs with a linear cost subject to linear matrix inequality (LMI) constraints along with nonlinear equality constraints representing a matrix inversion condition. The special feature of our algorithm is that only equality constraints are included in the augmented Lagrangian, while LMI constraints are kept explicitly in order to exploit currently available semi definite programming (SDP) codes. The step computation in the tangent problem is based on a Gauss–Newton model, and a specific line search and a first‐order Lagrange multiplier update rule are used to enhance efficiency. A number of computational results are reported and underline the strong practical performance of the algorithm. Copyright © 2003 John Wiley & Sons, Ltd.     

An ellipsoid algorithm for equality-constrained nonlinear programs

This paper describes an ellipsoid algorithm that solves convex problems having linear equality constraints with or without inequality constraints. Experimental results show that the new method is also effective for some problems that have nonlinear equality constraints or are otherwise nonconvex.Scope and purposeThe purpose of this paper is to present a variant of the ellipsoid algorithm that can be used with equalities. This is a significant improvement over the classical algorithm, which yields accurate solutions to convex and many nonconvex nonlinear programming problems but requires the feasible set to be of full dimension and therefore cannot be used with equality constraints.     

一种新的遗传算法求解有等式约束的优化问题

针对有等式约束的优化问题,提出了一种新的遗传算法.该算法是在种群初始化、交叉、变异操作过程中使用求解参数方程的方法处理等式约束,违反不等式约束的个体用死亡罚函数进行惩罚设计出的实数编码遗传算法.数值实验结果表明,新算法性能优于现有其它算法;它不仅可以处理线性等式约束,而且还可以处理非线性等式约束,同时提高了收敛速度和解的精度,是一种通用强、高效稳健的智能算法.     

Designer’s preferences regarding equality constraints in robust design optimization

Robust design optimization (RDO) problems can generally be formulated by appropriately incorporating uncertainty into the corresponding deterministic optimization problems. Equality constraints in the deterministic problem need to be carefully formulated into the RDO problem because of the strictness associated with their feasibility. In this context, equality constraints have been generally classified into two types: (1) those that must be satisfied regardless of uncertainty, examples include physics-based constraints, such as F = ma, and (2) those that cannot be satisfied because of uncertainty, which are typically designer-imposed, such as dimensional constraints. This paper addresses the notion of preferred degree of satisfaction of deterministic equality constraints under uncertainty. Whether or not a particular equality constraint can be exactly satisfied depends on the statistical nature of the design variables that exist in the constraint and on the designer’s preferences. In this context, this paper puts forth three contributions. First, we develop a comprehensive classification of equality constraints in a way that is mutually exclusive and collectively exhaustive. Second, we present a rank-based matrix approach to interactively classify equality constraints, which systematically incorporates the designer’s preferences into the classification process. Third, we present an approach to incorporate the designer’s intra-constraint and inter-constraint preferences for designer-imposed constraints into the RDO formulation. Intra-constraint preference expresses how closely a designer wishes to satisfy a particular constraint; for example, in terms of its mean and standard deviation. A designer may express inter-constraint preference if satisfaction of a particular designer-imposed constraint is more important than that of another. We present an optimization formulation that incorporates the above discussed constraint preferences, which provides the designer with the means to explore design space possibilities. The formulation entails interesting implications in terms of decision making. Two engineering examples are provided to illustrate the practical usefulness of the developments proposed in this paper.     

An adaptive penalty based covariance matrix adaptation–evolution strategy

Although most of unconstrained optimization problems with moderate to high dimensions can be easily handled with Evolutionary Computation (EC) techniques, constraint optimization problems (COPs) with inequality and equality constraints are very hard to deal with. Despite the fact that only equality constraints can be used to eliminate a certain variable, both types of constraints implicitly enforce a relation between problem variables. Most conventional constraint handling methods in EC do not consider the correlations between problem variables imposed by the problem constraints. This paper relies on the idea that a proper genetic operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. With this in mind, we employ a (μ+λ)-Evolution Strategy with a simplified variant of Covariance Matrix Adaptation based mutation operator along an adaptive weight adjustment scheme. The proposed algorithm is tested on two test sets. The outperformance of the algorithm is significant on the first benchmark when compared with five conventional methods. The results on the second test set show that algorithm is highly competitive when benchmarked with three state-of-art algorithms. The main drawback of the algorithm is its slightly lower speed of convergence for problems with high dimension and/or large search domain.     

FETI based algorithms for contact problems: scalability,large displacements and 3D Coulomb friction

Theoretical and experimental results concerning FETI based algorithms for contact problems of elasticity are reviewed. A discretized model problem is first reduced by the duality theory of convex optimization to the quadratic programming problem with bound and equality constraints. The latter is then optionally modified by means of orthogonal projectors to the natural coarse space introduced by Farhat and Roux in the framework of their FETI method. The resulting problem is then solved either by special algorithms for bound constrained quadratic programming problems combined with penalty that imposes the equality constraints, or by an augmented Lagrangian type algorithm with the inner loop for the solution of bound constrained quadratic programming problems. Recent theoretical results are reported that guarantee certain optimality and scalability of both algorithms. The results are confirmed by numerical experiments. The performance of the algorithm in solution of more realistic engineering problems by basic algorithm is demonstrated on the solution of 3D problems with large displacements or Coulomb friction.     

A computational method for parameter optimization problems arising in control†

A parameter optimization procedure is presented for large-scale problems arising in linear control system design that include equality and inequality constraints. The procedure is based on a novel min—max algorithm for locating a constrained relative minimum without the use of penalty functions or slack variables. This algorithm is constructed from an auxiliary minimization problem with equality constraints. Inequality constraints then are introduced using the notion of an effective constraint. Typical problem formulations are discussed, and an extensive design example is presented.     

Numerical experience with a derivative-free trust-funnel method for nonlinear optimization problems with general nonlinear constraints

A trust-funnel method is proposed for solving nonlinear optimization problems with general nonlinear constraints. It extends the one presented by Gould and Toint [Nonlinear programming without a penalty function or a filter. Math. Prog. 122(1):155–196, 2010], originally proposed for equality-constrained optimization problems only, to problems with both equality and inequality constraints and where simple bounds are also considered. As the original one, our method makes use of neither filter nor penalty functions and considers the objective function and the constraints as independently as possible. To handle the bounds, an active-set approach is employed. We then exploit techniques developed for derivative-free optimization (DFO) to obtain a method that can also be used to solve problems where the derivatives are unavailable or are available at a prohibitive cost. The resulting approach extends the DEFT-FUNNEL algorithm presented by Sampaio and Toint [A derivative-free trust-funnel method for equality-constrained nonlinear optimization. Comput. Optim. Appl. 61(1):25–49, 2015], which implements a derivative-free trust-funnel method for equality-constrained problems. Numerical experiments with the extended algorithm show that our approach compares favourably to other well-known model-based algorithms for DFO.     

相接行凸约束网络的快速识别算法

约束网络为计算机科学中的许多问题提供了一种有效的表示方法.一般而言,约束满足问题是NP完全的.然而,许多实际问题通常对约束的结构或形式施加了特殊的限制,从而能够高效地加以解决.迄今,为了识别易处理约束类,人们对特殊的约束或约束网络方面进行了许多研究.相接行凸(connected row-convex,简称CRC)约束网络是Deville等人提出的一类易处理问题.为了给该类问题寻求有效的快速识别算法,在CRC约束网络相关工作基础上,提出了CRC约束矩阵的标准型.在分析CRC约束矩阵的标准型性质的基础上,利用行凸(row-convex,简称RC)约束网络的判定,结合PQ树(由P节点和Q节点构成的树)的性质和矩阵的索引表示法,给出了CRC约束网络的快速识别算法.该算法的时间复杂度为O(n³d²),其中,n为约束网络涉及的变量数,d为各变量的定义域中最大定义域的大小.该时间复杂度达到该类问题的最佳时间复杂度,从而为实际的CRC约束满足问题的求解提供了可行的方法.     

一个通用的混合非线性规划问题的演化算法

提出了一种新的求解非线性规划问题的演化算法，它是在郭涛算法的基础上提出的，新算法的主要特点是引入了变维子空间，加入了子空间搜索过程和规范化约束条件以及增加了处理带等式约束的实数规划，整数规划，0－1规划和混合整数规划问题的功能，使之成为一种求解非线性规划（NLP）问题的通用算法，数值实验表明，新算法不仅是一种通用的算法，而且与已有算法的计算结果相比，其解的精确度也最好。     

An adaptive differential evolutionary algorithm incorporating multiple mutation strategies for the economic load dispatch problem

As growing the demand for electrical energy, economic load dispatch (ELD) has become one of the most important and complex issues in the operation of power systems. Owing to the confined optimum convergence and the additional constraints, it does not proficient to crack such problems by the predictable optimization algorithms. In this paper, a self-adaptable differential evolution algorithm integrating with multiple mutation strategies (ADE-MMS) is proposed for the ELD problems. In order to improve the exploration and exploitation capabilities of the original differential evolution algorithm (DE), ADE-MMS has three extensions to DE. Firstly, four types of advanced vectors generated by the different methods are employed in the mutation strategies. Secondly, a self-adaptable selection mechanism for the multiple mutation strategies is implemented in the iterations. Thirdly, the main control parameters are updated according to the fitness value under the tolerance threshold. Additionally, an effective repair method is proposed to handle the equality constraints of the ELD problems. ADE-MMS not only improve the convergence speed of the original DE but also keep equilibrium state between the exploration and the exploration. A tolerance threshold for the main control parameters makes the original DE more adaptive. Moreover, the modified equality constraints handling method is benefit to meet the equality constraints and minimize the impact on the algorithm. The performances of four DE algorithms are tested on the ten ELD problems with diverse complexities. Experimental results and comparisons with other recently reported ELD algorithms confirm that ADE-MMS is capable of obtaining excellent and feasible solutions. It reveal that ADE-MMS has good potential to solvating the ELD problems.     

求解约束优化问题的协同进化教与学优化算法

对约束优化问题,为了避免罚因子和等式约束转化为不等式约束时引入的约束容忍度参数所带来的不便,本文在基本教与学优化（Teaching-learning-based optimization,TLBO）算法中加入了自我学习过程并提出了一种求解约束优化问题的协同进化教与学优化算法,使得罚因子和约束容忍度随种群的进化动态调整.对7个常见测试函数的数值实验验证了算法求解带有等式和不等式约束优化问题的有效性.     

A constraint-space conjugate gradient method for function minimization and optimal control problems†

First, an algorithm is presented for minimizing an algebraic function subject to general algebraic equality constraints. The algorithm is based formally on the conjugate gradient method for solving unconstrained minimization problems but has the property that each iterate satisfies the constraint conditions exactly. Next, an extension of the algorithm is given which makes it applicable to optimal control problems with terminal state constraints. The computational characteristics of the method are demonstrated with numerical examples.     

基于梯度投影法与随机优化算法的约束优化方法

针对带有线性等式和不等式约束的无确定函数形式的约束优化问题,提出一种利用梯度投影法与遗传算法、同时扰动随机逼近等随机算法相结合的优化方法。该方法利用遗传算法进行全局搜索,利用同时扰动随机逼近算法进行局部搜索,算法在每次进化时根据线性约束计算父个体处的梯度投影方向,以产生新个体,从而能够严格保证新个体满足全部约束条件。将上述约束优化算法应用于典型约束优化问题,其仿真结果表明了所提出算法的可行性和收敛性。     

An intelligent moving object optimization algorithm for design problems with mixed variables, mixed constraints and multiple objectives

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables; a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints; and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder–Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method.     

Internal positivity preserved model reduction

This article studies model reduction of continuous-time stable positive linear systems under the Hankel norm, H _∞ norm and H ₂ norm performance. The reduced-order systems preserve the stability as well as the positivity of the original systems. This is achieved by developing new necessary and sufficient conditions of the model reduction performances in which the Lyapunov matrices are decoupled with the system matrices. In this way, the positivity constraints in the reduced-order model can be imposed in a natural way. As the model reduction performances are expressed in linear matrix inequalities with equality constraints, the desired reduced-order positive models can be obtained by using the cone complementarity linearisation iterative algorithm. A numerical example is presented to illustrate the effectiveness of the given methods.     

A scalable FETI-DP algorithm for a semi-coercive variational inequality

We develop an optimal algorithm for the numerical solution of semi-coercive variational inequalities by combining dual-primal FETI algorithms with recent results for bound and equality constrained quadratic programming problems. The discretized version of the model problem, obtained by using the FETI-DP methodology, is reduced by the duality theory of convex optimization to a quadratic programming problem with bound and equality constraints, which is solved by a new algorithm with a known rate of convergence given in terms of the spectral condition number of the quadratic problem. We present convergence bounds that guarantee the scalability of the algorithm. These results are confirmed by numerical experiments.     

Geometrically-Aware Interactive Object Manipulation

This paper describes formulation and management of constraints, and a nonlinear optimization algorithm that together enable interactive geometrically aware manipulation of articulated objects. Going beyond purely kinematic or dynamic approaches, our solution method directly employs geometric constraints to ensure non-interpenetration during object manipulation. We present the formulation of the inequality constraints used to ensure nonpenetration, describe how to manage the set of active inequality constraints as objects move, and show how these results are combined with a nonlinear optimization algorithm to achieve interactive geometrically aware object manipulation. Our optimization algorithm handles equality and inequality constraints and does not restrict object topology. It is an efficient iterative algorithm, quadratically convergent, with each iteration bounded by O ( n _nz ( L )), where n _nz ( L ) is the number of non-zeros in L , a Cholesky factor of a sparse matrix.     

Exact fuzzy optimal solution of fully fuzzy linear programming problems with unrestricted fuzzy variables

Kumar et al. (Appl. Math. Model. 35:817?C823, 2011) pointed out that there is no method in literature to find the exact fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems and proposed a new method to find the fuzzy optimal solution of FFLP problems with equality constraints having non-negative fuzzy variables and unrestricted fuzzy coefficients. There may exist several FFLP problems with equality constraints in which no restriction can be applied on all or some of the fuzzy variables but due to the limitation of the existing method these types of problems can not be solved by using the existing method. In this paper a new method is proposed to find the exact fuzzy optimal solution of FFLP problems with equality constraints having non-negative fuzzy coefficients and unrestricted fuzzy variables. The proposed method can also be used to solve the FFLP problems with equality constraints having non-negative fuzzy variables and unrestricted fuzzy coefficients. To show the advantage of the proposed method over existing method the results of some FFLP problems with equality constraints, obtained by using the existing and proposed method, are compared. Also, to show the application of proposed method a real life problem is solved by using the proposed method.     

A Robust Algorithm for Solving Nonlinear Programming Problems

In this paper, we introduce a new algorithm for solving nonlinear programming (NLP) problems. It is an extension of Guo's algorithm [1] which possesses enhanced capabilities for solving NLP problems. These capabilities include: a) extending the variable subspace, b) adding a search process over subspaces and normalized constraints, c) using an adaptive penalty function, and d) adding the ability to deal with integer NLP problems, 0-1 NLP problems, and mixed-integer NLP problems which have equality constraints. These four enhancements increase the capabilities of the algorithm to solve nonlinear programming problems in a more robust and universal way. This paper will present results of numerical experiments which show that the new algorithm is not only more robust and universal than its competitors, but also its performance level is higher than any others in the literature.