An algorithm for semi-infinite transportation problems

In this paper we consider a class of semi-infinite transportation problems. We develop an algorithm for this class of semi-infinite transportation problems. The algorithm is a primal dual method which is a generalization of the classical algorithm for finite transportation problems. The most important aspect of our paper is that we can prove the convergence result for the algorithm. Finally, we implement some examples to illustrate our algorithm.     

求解多项式规划的一个全局最优化算法

提出一个求解带箱子约束的一般多项式规划问题的全局最优化算法, 该算法包含两个阶段, 在第一个阶段, 利用局部最优化算法找到一个局部最优解. 在第二阶段, 利用一个在单位球上致密的向量序列, 将多元多项式转化为一元多项式, 通过求解一元多项式的根, 找到一个比当前局部最优解更好的点作为初始点, 回到第一个 阶段, 从而得到一个更好的局部最优解, 通过两个阶段的循环最终找到问题的全局最优解, 并给出了算法收敛性分析. 最后, 数值结果表明了算法是有效的.     

A recursive quadratic programming algorithm for semi-infinite optimization problems

The well known, local recursive quadratic programming method introduced by E. R. Wilson is extended to apply to optimization problems with constraints of the type , where ranges over a compact interval of the real line. A scheme is proposed, which results in a globally convergent conceptual algorithm. Finally, two implementable versions are presented both of which converge quadratically.Research sponsored by the National Science Foundation Grant ECS-79-13148 and the Air Force Office of Scientific Research (AFOSR) United States Air Force Contract No. F49620-79-C-0178     

Semidefinite relaxations for semi-infinite polynomial programming

This paper studies how to solve semi-infinite polynomial programming (SIPP) problems by semidefinite relaxation methods. We first recall two SDP relaxation methods for solving polynomial optimization problems with finitely many constraints. Then we propose an exchange algorithm with SDP relaxations to solve SIPP problems with compact index set. At last, we extend the proposed method to SIPP problems with noncompact index set via homogenization. Numerical results show that the algorithm is efficient in practice.     

An exact penalty function algorithm for semi-infinite programmes

An algorithm for semi-inifinite programming using sequential quadratic programming techniques together with anL exact penalty function is presented, and global convergence is shown. An important feature of the convergence proof is that it does not require an implicit function theorem to be applicable to the semi-infinite constraints; a much weaker assumption concerning the finiteness of the number of global maximizers of each semi-infinite constraint is sufficient. In contrast to proofs based on an implicit function theorem, this result is also valid for a large class ofC ¹ problems.     

An interior point algorithm for semi-infinite linear programming

We consider the generalization of a variant of Karmarkar's algorithm to semi-infinite programming. The extension of interior point methods to infinite-dimensional linear programming is discussed and an algorithm is derived. An implementation of the algorithm for a class of semi-infinite linear programs is described and the results of a number of test problems are given. We pay particular attention to the problem of Chebyshev approximation. Some further results are given for an implementation of the algorithm applied to a discretization of the semi-infinite linear program, and a convergence proof is given in this case.     

An exact Jacobian SDP relaxation for polynomial optimization

Given polynomials f (x), g _i (x), h _j (x), we study how to minimize f (x) on the set $$S = \left\{ x \in \mathbb{R}^n:\, h_1(x) = \cdots = h_{m_1}(x) = 0,\\ g_1(x)\geq 0, \ldots, g_{m_2}(x) \geq 0 \right\}.$$ Let f _min be the minimum of f on S. Suppose S is nonsingular and f _min is achievable on S, which are true generically. This paper proposes a new type semidefinite programming (SDP) relaxation which is the first one for solving this problem exactly. First, we construct new polynomials ${\varphi_1, \ldots, \varphi_r}$ , by using the Jacobian of f, h _i , g _j , such that the above problem is equivalent to $$\begin{gathered}\underset{x\in\mathbb{R}^n}{\min} f(x) \hfill \\ \, \, {\rm s.t.}\; h_i(x) = 0, \, \varphi_j(x) = 0, \, 1\leq i \leq m_1, 1 \leq j \leq r, \hfill \\ \quad \, \, \, g_1(x)^{\nu_1}\cdots g_{m_2}(x)^{\nu_{m_2}}\geq 0, \, \quad\forall\, \nu \,\in \{0,1\}^{m_2} .\hfill \end{gathered}$$ Second, we prove that for all N big enough, the standard N-th order Lasserre’s SDP relaxation is exact for solving this equivalent problem, that is, its optimal value is equal to f _min. Some variations and examples are also shown.     

Global optimization algorithm for sum of generalized polynomial ratios problem

In this paper, a global optimization algorithm is proposed for solving sum of generalized polynomial ratios problem (P) which arises in various practical problems. Due to its intrinsic difficulty, less work has been devoted to globally solve the problem (P). For such problems, we present a branch and bound algorithm. In this method, by utilizing exponent transformation and new three-level linear relaxation method, a sequence of linear relaxation programming of the initial nonconvex programming problem (P) are derived which are embedded in a branch and bound algorithm. The proposed method need not introduce new variables and constraints and it is convergent to the global minimum of prime problem by means of the subsequent solutions of a series of linear programming problems. Several numerical examples in the literatures are tested to demonstrate that the proposed algorithm can systematically solve these examples to find the approximate ?-global optimum.     

A polynomial primal-dual affine scaling algorithm for symmetric conic optimization

The primal-dual Dikin-type affine scaling method was originally proposed for linear optimization and then extended to semidefinite optimization. Here, the method is generalized to symmetric conic optimization using the notion of Euclidean Jordan algebras. The method starts with an interior feasible but not necessarily centered primal-dual solution, and it features both centering and reducing the duality gap simultaneously. The method’s iteration complexity bound is analogous to the semidefinite optimization case. Numerical experiments demonstrate that the method is viable and robust when compared to SeDuMi, MOSEK and SDPT3.     

An asymptotically exact polynomial algorithm for equipartition problems

We describe an approximate algorithm for a special ‘quadratic semi-assignment problem’ arising from ‘equipartition’ applications, where one wants to cluster n objects with given weights w_i into p classes, so as to minimize the variance of the class-weights. The algorithm can be viewed both as a list scheduling method and as a special case of a heuristic procedure, due to Nemhauser and Carlson, for quadratic semi-assignment problems. Our main result is that the relative approximation error is O(1/n) when p and r = (maxw_i)/(min w_i) are bounded.     

An accelerated central cutting plane algorithm for linear semi-infinite programming

An algorithm for linear semi-infinite programming is presented which accelerates the convergence of the central cutting plane algorithm first proposed in [4]. Compared with other algorithms, the algorithm in [4] has the advantage of being applicable under mild conditions and of providing feasible solutions. However its convergence has been shown to be rather slow in practical instances. The algorithm proposed in this paper introduces a simple acceleration scheme which gives faster convergence, as confirmed by several examples, as well as an interval of prefixed length containing the optimum value. It is also shown that the algorithm provides a solution of the dual problem and that it can be used for convex semi-infinite programming too.Mathematics Subject Classification (1991): 90C05, 90C34, 65K05, 90C51Acknowledgments. The author whishes to thank the three anonymous referees and an associate editor for many useful comments and valuable suggestions.     

An algorithm for complex linear approximation based on semi-infinite programming

An algorithm is described for computing the best linear Chebyshev approximation for functions in the complex plane. Implementation and convergence are shown and numerical examples are given.     

An extension of the simplex algorithm for semi-infinite linear programming

We present a primal method for the solution of the semi-infinite linear programming problem with constraint index setS. We begin with a detailed treatment of the case whenS is a closed line interval in . A characterization of the extreme points of the feasible set is given, together with a purification algorithm which constructs an extreme point from any initial feasible solution. The set of points inS where the constraints are active is crucial to the development we give. In the non-degenerate case, the descent step for the new algorithm takes one of two forms: either an active point is dropped, or an active point is perturbed to the left or right. We also discuss the form of the algorithm when the extreme point solution is degenerate, and in the general case when the constraint index set lies in ^p . The method has associated with it some numerical difficulties which are at present unresolved. Hence it is primarily of interest in the theoretical context of infinite-dimensional extensions of the simplex algorithm.     

An approximation of feasible sets in semi-infinite optimization

The paper deals with the feasible setM of a semi-infinite optimization problem, i.e.M is a subset of the finite-dimensional Euclidean space and is basically defined by infinitely many inequality constraints. Assuming that the extended Mangasarian-Fromovitz constraint qualification holds at all points fromM, it is shown that the quadratic distance function with respect toM is continuously differentiable on an open neighborhood ofM. If, in addition,M is compact, then the set , which is described by this quadratic distance function, is shown to be an appropriate approximation ofM and the setsM and can be topologically identified via a homeomorphism.     

An algorithm for nondifferentiable optimization

In this paper, we give an implementable algorithm for minimizing a locally Lipschitz function without constraints, and prove the global convergence under the -acute angle condition.     

The empirical performance of a polynomial algorithm for constrained nonlinear optimization

In [6], a polynomial algorithm based on successive piecewise linear approximation was described. The algorithm is polynomial for constrained nonlinear (convex or concave) optimization, when the constraint matrix has a polynomial size subdeterminant. We propose here a practical adaptation of that algorithm with the idea of successive piecewise linear approximation of the objective on refined grids, and the testing of the gap between lower and upper bounds. The implementation uses the primal affine interior point method at each approximation step. We develop special features to speed up each step and to evaluate the gap. Empirical study of problems of size up to 198 variables and 99 constraints indicates that the procedure is very efficient and all problems tested were terminated after 171 interior point iterations. The procedure used in the implementation is proved to converge when the objective is strongly convex.Supported in part by the Office of Naval Research under Grant No. N00014-88-K-0377 and Grant No. ONR N00014-91-J-1241.     

An approximating polynomial algorithm for a sequence partitioning problem

We consider a strongly NP-hard problem of partitioning a finite sequence of vectors in Euclidean space into two clusters using the criterion of minimum sum-of-squares of distances from the elements of clusters to their centers. We assume that the cardinalities of the clusters are fixed. The center of one cluster has to be optimized and is defined as the average value over all vectors in this cluster. The center of the other cluster lies at the origin. The partition satisfies the condition: the difference of the indices of the next and previous vectors in the first cluster is bounded above and below by two given constants. We propose a 2-approximation polynomial algorithm to solve this problem.     

An algorithm for unimodular completion over Laurent polynomial rings

We present a new and simple algorithm for completion of unimodular vectors with entries in a multivariate Laurent polynomial ring over an infinite field K. More precisely, given n?3 and a unimodular vector V=^t(v₁,…,v_n)∈Rⁿ (that is, such that 〈v₁,…,v_n〉=R), the algorithm computes a matrix M in M_n(R) whose determinant is a monomial such that MV=^t(1,0,…,0), and thus M^-1 is a completion of V to an invertible matrix.     

An efficient algorithm for solving semi-infinite inequality problems with box constraints

Many design objectives may be formulated as semi-infinite constraints. Examples in control design, for example, include hard constraints on time and frequency responses and robustness constraints. A useful algorithm for solving such inequalities is the outer approximations algorithm. One version of an outer approximations algorithm for solving an infinite set of inequalities(x, y) 0 for allyY proceeds by solving, at iterationi of the master algorithm, a finite set of inequalities ((x, y) 0 for allyY _i) to yieldx _i and then updatingY _i toY _i+1=Y _i {y_i } wherey _i arg max {(x _i,y)¦y Y}. Since global optimization is computationally extremely expensive, it is desirable to reduce the number of such optimizations. We present, in this paper, a modified version of the outer approximations algorithm which achieves this objective.The research reported herein was sponsored by the National Science Foundation Grants ECS-9024944, ECS-8816168, the Air Force Office of Scientific Research Contract AFOSR-90-0068, and the NSERC of Canada under Grant OGPO-138352.     

A global optimization algorithm for polynomial programming problems using a Reformulation-Linearization Technique

This paper is concerned with the development of an algorithm to solve continuous polynomial programming problems for which the objective function and the constraints are specified polynomials. A linear programming relaxation is derived for the problem based on a Reformulation Linearization Technique (RLT), which generates nonlinear (polynomial) implied constraints to be included in the original problem, and subsequently linearizes the resulting problem by defining new variables, one for each distinct polynomial term. This construct is then used to obtain lower bounds in the context of a proposed branch and bound scheme, which is proven to converge to a global optimal solution. A numerical example is presented to illustrate the proposed algorithm.