A unified aggregation and relaxation approach for stress-constrained topology optimization

In this paper, we propose a unified aggregation and relaxation approach for topology optimization with stress constraints. Following this approach, we first reformulate the original optimization problem with a design-dependent set of constraints into an equivalent optimization problem with a fixed design-independent set of constraints. The next step is to perform constraint aggregation over the reformulated local constraints using a lower bound aggregation function. We demonstrate that this approach concurrently aggregates the constraints and relaxes the feasible domain, thereby making singular optima accessible. The main advantage is that no separate constraint relaxation techniques are necessary, which reduces the parameter dependence of the problem. Furthermore, there is a clear relationship between the original feasible domain and the perturbed feasible domain via this aggregation parameter.     

A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense that any optimum of the objective function with bound constraints is also an equilibrium point of the neural network. If the objective function to be minimized is convex, then the recurrent neural network is complete in the sense that the set of optima of the function with bound constraints coincides with the set of equilibria of the neural network. 2) The recurrent neural network is primal and quasiconvergent in the sense that its trajectory cannot escape from the feasible region and will converge to the set of equilibria of the neural network for any initial point in the feasible bound region. 3) The recurrent neural network has an attractivity property in the sense that its trajectory will eventually converge to the feasible region for any initial states even at outside of the bounded feasible region. 4) For minimizing any strictly convex quadratic objective function subject to bound constraints, the recurrent neural network is globally exponentially stable for almost any positive network parameters. Simulation results are given to demonstrate the convergence and performance of the proposed recurrent neural network for nonlinear optimization with bound constraints.     

The scenario approach to robust control design

This paper proposes a new probabilistic solution framework for robust control analysis and synthesis problems that can be expressed in the form of minimization of a linear objective subject to convex constraints parameterized by uncertainty terms. This includes the wide class of NP-hard control problems representable by means of parameter-dependent linear matrix inequalities (LMIs). It is shown in this paper that by appropriate sampling of the constraints one obtains a standard convex optimization problem (the scenario problem) whose solution is approximately feasible for the original (usually infinite) set of constraints, i.e., the measure of the set of original constraints that are violated by the scenario solution rapidly decreases to zero as the number of samples is increased. We provide an explicit and efficient bound on the number of samples required to attain a-priori specified levels of probabilistic guarantee of robustness. A rich family of control problems which are in general hard to solve in a deterministically robust sense is therefore amenable to polynomial-time solution, if robustness is intended in the proposed risk-adjusted sense.     

CIMS下单级单资源约束的生产批量计划问题的新算法

对单级单资源约束的生产批量计划问题采用Ｌａｇｒａｎｇｉａｎ松弛算法进行求解,对能力约束进行松弛后的Ｌａｇｒａｎｇｉａｎ问题的求解,构造了新的启发式算法,在用Ｌａｇｒａｎｇｉａｎ松驰问题获得原问题的可行解时,提出了多回路启动式算法,仿真实验结果表明,平均相对对偶间隙可在２％以内。     

The binomial decomposition of OWA functions,the 2‐additive and 3‐additive cases in n dimensions

In the context of the binomial decomposition of ordered weighted averaging (OWA) functions, we investigate the constraints associated with the 2‐additive and 3‐additive cases in n dimensions. The 2‐additive case depends on one coefficient whose feasible region does not depend on the dimension n. On the other hand, the feasible region of the 3‐additive case depends on two coefficients and is explicitly dependent on the dimension n. This feasible region is a convex polygon with n vertices and n edges, which is strictly expanding in the dimension n. The orness of the OWA functions within the feasible region is linear in the two coefficients, and the vertices associated with maximum and minimum orness are identified. Finally, we discuss the 3‐additive binomial decomposition in the asymptotic infinite dimensional limit.     

Iteration complexity analysis of dual first-order methods for conic convex programming

In this paper we provide a detailed analysis of the iteration complexity of dual first-order methods for solving conic convex problems. When it is difficult to project on the primal feasible set described by conic and convex constraints, we use the Lagrangian relaxation to handle the conic constraints and then, we apply dual first-order algorithms for solving the corresponding dual problem. We give convergence analysis for dual first-order algorithms (dual gradient and fast gradient algorithms): we provide sublinear or linear estimates on the primal suboptimality and feasibility violation of the generated approximate primal solutions. Our analysis relies on the Lipschitz property of the gradient of the dual function or an error bound property of the dual. Furthermore, the iteration complexity analysis is based on two types of approximate primal solutions: the last primal iterate or an average primal sequence.     

A variable structure convex programming based control approach for a class of uncertain linear systems

A variable structure convex programming based control for a class of linear uncertain systems with accessible state is presented in this note. A convex programming problem is solved, on-line, by reformulating the problem in terms of a piecewise smooth penalty function, and relying on a suitable analog variable structure system implementing the gradient procedure. In this way, the controlled system reference movement, optimal with respect to a pre-specified scalar convex cost function and a set of suitable equality and inequality constraints, is generated. An inner control loop aimed at the finite time exact tracking of the reference movement is also designed. As a result, the controlled system trajectory starting in the feasible region there remains, and the optimal movement in the feasible region is proved to be an asymptotically stable equilibrium point of the controlled system.     

Lossless convexification of a class of optimal control problems with non-convex control constraints

We consider a class of finite time horizon optimal control problems for continuous time linear systems with a convex cost, convex state constraints and non-convex control constraints. We propose a convex relaxation of the non-convex control constraints, and prove that the optimal solution of the relaxed problem is also an optimal solution for the original problem, which is referred to as the lossless convexification of the optimal control problem. The lossless convexification enables the use of interior point methods of convex optimization to obtain globally optimal solutions of the original non-convex optimal control problem. The solution approach is demonstrated on a number of planetary soft landing optimal control problems.     

Bicriterion linear programming

Basically leaning on the concept of “best” compromise, the technique seeks the optimal solution by fair relaxations of the objectives commensurate with their degrees of importance until the optimum feasible compromise is reached. The concept is made operational by deriving a linear constraint (referred to as the compromise constraint) to be added into the original set. An offspring of both objective functions, the compromise constraint cuts the original feasible region and forces both objectives to settle on a common point along this added restriction. The resulting singular equivalent of the bicriterion problem optimizes any one of the two objective functions and their equivalent sum (referred to as the supergoal) subject to the new set of constraints. Post optimality analysis is employed to minimize the computational effort usually done by a computer. A very attractive feature of this new practical technique is its ability to search for the optimum in any point in the feasible region, even other than the vertices of the convex set.     

Extension of a hybrid Genetic Algorithm for nonlinear programming problems with equality and inequality constraints

As an extension of the hybrid Genetic Algorithm-HGA proposed by Tang et al. (Comput. Math. Appl. 36 (1998) 11), this paper focuses on the critical techniques in the application of the GA to nonlinear programming (NLP) problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible points/chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are proposed to formulate and evaluate the infeasible chromosomes. The extended version of concepts of dominated semi-feasible direction (DSFD), feasibility degree (FD₁) of semi-feasible direction, feasibility degree (FD₂) of infeasible points ‘belonging to’ feasible domain are introduced. Combining the new evaluation functions and weighted gradient direction search into the Genetic Algorithm, an extended hybrid Genetic Algorithm (EHGA) is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints. Simulation shows that this new algorithm is efficient.Scope and purposeNon-linear Programming (NLP) problems with equality and inequality constraints is an important type of constrained optimization problems. Genetic Algorithm (GA) is one of the well known evolutionary computation techniques. In the application of GA to NLP problems, chromosomes randomly generated at the beginning and/or generated by genetic operators during the evolutionary process usually violate the constraints, resulting in infeasible chromosomes. Therefore, the handling of system constraints, particularly the nonlinear equation constraints, and the measurement and evaluation of infeasible chromosomes, are major concerns in GA. Penalty strategy in the construction of fitness function is commonly used to evaluate the infeasible chromosomes in some traditional AG methods. However, this approach essentially narrows down the search space by eliminating all infeasible chromosomes from the evolutionary process, and it may reduce the chances of finding better candidates for the global optimization. In particular, it absolutely ignores the information carried by the infeasible chromosomes itself. Therefore, formulating the infeasible chromosomes by embedding the relevant information into the evaluation function are important when applying GA to NLP.As an extension of the Hybrid Genetic Algorithm-HGA proposed by Tang et al. (1998), this paper focuses on the critical techniques in the application of GA to NLP problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are designed to formulate and evaluate the infeasible chromosomes. By introducing an extended version of the concepts of dominated semi-feasible direction (DSFD), feasibility degree (FD₁) of semi-feasible direction, feasibility degree (FD₂) of infeasible points ‘belonging to’ feasible domain, an extended hybrid Genetic Algorithm (EHGA) is developed for solving NLP problems with equality and inequality constraints.     

A perspective-based convex relaxation for switched-affine optimal control

We consider the switched-affine optimal control problem, i.e., the problem of selecting a sequence of affine dynamics from a finite set in order to minimize a sum of convex functions of the system state. We develop a new reduction of this problem to a mixed-integer convex program (MICP), based on perspective functions. Relaxing the integer constraints of this MICP results in a convex optimization problem, whose optimal value is a lower bound on the original problem value. We show that this bound is at least as tight as similar bounds obtained from two other well-known MICP reductions (via conversion to a mixed logical dynamical system, and by generalized disjunctive programming), and our numerical study indicates it is often substantially tighter. Using simple integer-rounding techniques, we can also use our formulation to obtain an upper bound (and corresponding sequence of control inputs). In our numerical study, this bound was typically within a few percent of the optimal value, making it attractive as a stand-alone heuristic, or as a subroutine in a global algorithm such as branch and bound. We conclude with some extensions of our formulation to problems with switching costs and piecewise affine dynamics.     

Finite‐Time Optimal Formation Control for Second‐Order Multiagent Systems

This paper studies the problem of finite‐time optimal formation control for second‐order multiagent systems in situations where the formation time and/or the cost function need to be considered. The finite‐time optimal formation control laws are proposed for the cases with or without a leader, respectively. For the case of control being constrained, the time optimal formation problem is considered and an algorithm is designed to derive a feasible solution for the problem concerned. Although the feasible solution may not be optimal, it can provide a lower bound for time for the formation problem with control constraints. Once the given formation time is lower than this bound, the control constraints cannot be ensured. Finally, some numerical examples are given to illustrate the effectiveness of the theoretical results.     

大规模过程系统优化方法

基于非线性约束的序列界无约束极小化方法,对大规模过程系统稳态优化的序列界约束极小化方法(SBCMM)进行了研究.对工程模型引进松弛变量处理后,SBCMM的罚函数仅包含等式约束的惩罚项,不包含界约束及不等式约束的惩罚项.原问题的解由求解一系列界约束极小化子问题而非无约束极小化子问题来获得.最后,用一类规模可变的非线性规划...     

Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem

We consider a variational convex relaxation of a class of optimal partitioning and multiclass labeling problems, which has recently proven quite successful and can be seen as a continuous analogue of Linear Programming (LP) relaxation methods for finite-dimensional problems. While for the latter several optimality bounds are known, to our knowledge no such bounds exist in the infinite-dimensional setting. We provide such a bound by analyzing a probabilistic rounding method, showing that it is possible to obtain an integral solution of the original partitioning problem from a solution of the relaxed problem with an a priori upper bound on the objective. The approach has a natural interpretation as an approximate, multiclass variant of the celebrated coarea formula.     

变量带简单界约束的凸规划一个域分解算法

本文考虑求解非线性规划min{f(x):l≤x≤u,x∈R~n},其中 f(x)是可微凸函数,l,u是已知的n维列向量,x为n维列向量,该问题不仅是实际应用中出现的简单界约束最优化问题,而且相当一部分优化问题可以把变量限制在有意义的区间上,因此无论在理论方面还是在实际应用方面,都有必要研究此类问题,给出简便而有效的算法。     

Topology optimization of trusses with stress and local constraints on nodal stability and member intersection

A truss topology optimization problem under stress constraints is formulated as a Mixed Integer Programming (MIP) problem with variables indicating the existence of nodes and members. The local constraints on nodal stability and intersection of members are considered, and a moderately large lower bound is given for the cross-sectional area of an existing member. A lower-bound objective value is found by neglecting the compatibility conditions, where linear programming problems are successively solved based on a branch-and-bound method. An upper-bound solution is obtained as a solution of a Nonlinear Programming (NLP) problem for the topology satisfying the local constraints. It is shown in the examples that upper- and lower-bound solutions with a small gap in the objective value can be found by the branch-and-bound method, and the computational cost can be reduced by using the local constraints.     

Enhancing CLP branch and bound techniques for scheduling problems

In this paper, we propose a constraint logic programming (CLP) approach to the solution of a job shop scheduling problem in the field of production planning in orthopaedic hospital departments. A pure CLP on finite domain (CLP(FD)) approach to the problem has been developed, leading to disappointing results. In fact, although CLP(FD) has been recognized as a suitable tool for solving combinatorial problems, it presents some drawbacks for optimization problems. The main reason concerns the fact that CLP(FD) solvers do not effectively handle the objective function and cost‐based reasoning through the simple branch and bound scheme they embed. Therefore, we have proposed an improvement of the standard CLP branch and bound algorithm by exploiting some well‐known operations research results. The branch and bound we integrate in a CLP environment is based on the optimal solution of a relaxation of the original problem. In particular, the relaxation used for the job shop scheduling problem considered is the well‐known shifted bottleneck procedure considering single machine problems. The idea is to decompose the original problem into subproblems and solve each of them independently. Clearly, the solutions of each subproblem may violate constraints among different subproblems which are not taken into account. However, these solutions can be exploited in order to improve the pruning of the search space and to guide the search by defining cost‐based heuristics. The resulting algorithm achieves a significant improvement with respect to the pure CLP(FD) approach that enables the solution of problems which are one order of magnitude greater than those solved by a pure CLP(FD) algorithm. In addition, the resulting code is less dependent on the input data configuration. Copyright © 2001 John Wiley & Sons, Ltd.     

Semidefinite relaxation for linear programs with equilibrium constraints

In this paper, we present a semidefinite programming (SDP) relaxation for linear programs with equilibrium constraints (LPECs) to be used in a branch‐and‐bound (B&B) algorithm. The procedure utilizes the global optimal solution of LPECs and was motivated by the B&B algorithm proposed by Bard and Moore for linear/quadratic bilevel programs, where complementarities are recursively enforced. We propose the use of the SDP relaxation to generate bounds at the nodes of the B&B tree. Computational results compare the quality of the bounds given by the SDP relaxation with the ones given by conventional linear programming relaxations.     

A Branch and Bound Algorithm for An Uncapacitated Facility Location Problem with a Side Constraint

In this paper, a branch and bound algorithm for solving an uncapacitated facility location problem (UFLP) with an aggregate capacity constraint is presented. The problem arises as a subproblem when Lagrangean relaxation of the capacity constraints is used to solve capacitated facility location problems. The algorithm is an extension of a procedure used by Christofides and Beasley (A tree search algorithm for the p-median problem. European Journal of Operational Research , Vol. 10, 1982, pp. 196–204) to solve p -median problems and is based on Lagrangean relaxation in combination with subgradient optimization for lower bounding, simple Lagrangean heuristics to produce feasible solutions, and penalties to reduce the problem size. For node selection, a jump-backtracking rule is proposed, and alternative rules for choosing the branching variable are discussed. Computational experience is reported.     

Constrained model predictive control for time-varying delay systems: Application to an active car suspension

This study investigates the problem of robust model predictive control (RMPC) for active suspension systems with time-varying delays and input constraints. The uncertainty is of convex polytopic type. Based on the Lyapunov-Krasovskii functional method, sufficient stability conditions of the time-varying delays systems are derived by linear matrix inequalities (LMIs) terms. At each time set, a feasible state feedback is obtained by minimizing an upper bound of the ‘worst-case’ quadratic objective function over an infinite horizon subject to constraints on inputs. Finally, a quarter-vehicle model is exploited to demonstrate the effectiveness of the proposed method.