A robust algorithm for generalized geometric programming

Most existing methods of global optimization for generalized geometric programming (GGP) actually compute an approximate optimal solution of a linear or convex relaxation of the original problem. However, these approaches may sometimes provide an infeasible solution, or far from the true optimum. To overcome these limitations, a robust solution algorithm is proposed for global optimization of (GGP) problem. This algorithm guarantees adequately to obtain a robust optimal solution, which is feasible and close to the actual optimal solution, and is also stable under small perturbations of the constraints.     

Branch-reduction-bound algorithm for generalized geometric programming

This article presents a branch-reduction-bound algorithm for globally solving the generalized geometric programming problem. To solve the problem, an equivalent monotonic optimization problem whose objective function is just a simple univariate is proposed by exploiting the particularity of this problem. In contrast to usual branch-and-bound methods, in the algorithm the upper bound of the subproblem in each node is calculated easily by arithmetic expressions. Also, a reduction operation is introduced to reduce the growth of the branching tree during the algorithm search. The proposed algorithm is proven to be convergent and guarantees to find an approximative solution that is close to the actual optimal solution. Finally, numerical examples are given to illustrate the feasibility and efficiency of the present algorithm.     

Constrained duality via unconstrained duality in generalized geometric programming

A specialization of unconstrained duality (involving problems without explicit constraints) to constrained duality (involving problems with explicit constraints) provides an efficient mechanism for extending to the latter many important theorems that were previously established for the former.This research was sponsored by the Air Force Office of Scientific Research, Air Force Systems Command, USAF, under Grant No. AFOSR-73-2516.     

A new arc algorithm for unconstrained optimization

The gradient path of a real valued differentiable function is given by the solution of a system of differential equations. For a quadratic function the above equations are linear, resulting in a closed form solution. A quasi-Newton type algorithm for minimizing ann-dimensional differentiable function is presented. Each stage of the algorithm consists of a search along an arc corresponding to some local quadratic approximation of the function being minimized. The algorithm uses a matrix approximating the Hessian in order to represent the arc. This matrix is updated each stage and is stored in its Cholesky product form. This simplifies the representation of the arc and the updating process. Quadratic termination properties of the algorithm are discussed as well as its global convergence for a general continuously differentiable function. Numerical experiments indicating the efficiency of the algorithm are presented.     

A modified concave simplex algorithm for geometric programming

An algorithm for solving posynomial geometric programs is presented. The algorithm uses a modification of the concave simplex method to solve the dual program which has a nondifferentiable objective function. The method permits simultaneous changes in certain blocks of dual variables. A convergence proof follows from the convergence proof of the concave simplex method. Some computational results on problems with up to forty degrees of difficulty are included.     

求广义几何规划全局最优解的线性化方法

对广义几何规划问题(GGP)提出了一个确定型全局优化算法，这类优化问题能广泛应用于工程设计和非线性系统的鲁棒稳定性分析等实际问题中，使用指数变换及对目标函数和约束函数的线性下界估计，建立了GGP的松弛线性规划(RLP)，通过对RLP可行域的细分以及一系列RLP的求解过程，从理论上证明了算法能收敛到GGP的全局最优解，对一个化学工程设计问题应用本文算法，数值实验表明本文方法是可行的。     

A global optimization using linear relaxation for generalized geometric programming

Many local optimal solution methods have been developed for solving generalized geometric programming (GGP). But up to now, less work has been devoted to solving global optimization of (GGP) problem due to the inherent difficulty. This paper considers the global minimum of (GGP) problems. By utilizing an exponential variable transformation and the inherent property of the exponential function and some other techniques the initial nonlinear and nonconvex (GGP) problem is reduced to a sequence of linear programming problems. The proposed algorithm is proven that it is convergent to the global minimum through the solutions of a series of linear programming problems. Test results indicate that the proposed algorithm is extremely robust and can be used successfully to solve the global minimum of (GGP) on a microcomputer.     

A nonmonotone globalization algorithm with preconditioned gradient path for unconstrained optimization

The aim of this paper is to incorporate the preconditioned gradient path in a nonmonotone stabilization algorithm for unconstrained optimization. The global convergence and locally superlinear convergence are established for this class of algorithms. Finally, we report in details the numerical results which show the effectiveness of the proposed algorithm.     

Comparison of generalized geometric programming algorithms

Numerical results are presented of extensive tests involving five posynomial and twelve signomial programming codes. The set of test problems includes problems with a pure mathematical meaning as well as problems originating from different fields of engineering. The algorithms are compared on the basis of CPU time, number of failures, preparation time, and in-core storage.The authors wish to thank Messieurs M. Avriel, P. Beck, J. Bradley, R. Dembo, T. Jefferson, R. Sargent and A. Templeman for the possibility of using their respective codes in this study.     

Optimality conditions in generalized geometric programming

Generalizations of the Kuhn-Tucker optimality conditions are given, as are the fundamental theorems having to do with their necessity and sufficiency.This research was sponsored by the Air Force Office of Scientific Research, Air Force Systems Command, USAF, under Grant No. AFOSR-73-2516.     

A new algorithm for geometric programming based on the linear structure of its dual problem

In this article, we present an algorithm for the resolution of a nonlinear optimization problem, concretely the posynomial geometric programming model. The solution procedure that we develop extends the condensation techniques for geometric programming, allowing us to find the optimal solutions to the dual geometric problems that we get from the interior of the corresponding feasible regions, in the line that interior point methods for linear programming work, which leads us to obtain considerable computational advantages with respect of the classical solution procedures.     

A new integral filter algorithm for unconstrained global optimization

In this paper, making use a exponential integral filter, a new algorithm for unconstrained global optimization is proposed. Compared with Yang’s absolute value type integral filter method (Yang et al., Appl Math Comput 18:173–180, 2007), this algorithm is more effective and more sensitive. Numerical results for some typical examples show that in most cases, this algorithm works effectively and reliably.     

A new algorithm for quadratic programming

We present a new finite algorithm for quadratic programming. Our algorithm is based on the solution procedures of linear programming (pivoting, Bland's rule, Hungarian Methods, criss-cross method), however this method does not require the enlargement of the basic tableau as Frank-Wolfe method does. It can be considered as a feasible point active-set method. We solve linear equation systems in oder to reach an active constraint set (complementary solutions) and we solve a feasibility problem in order to check that optimality can be reached on this active set or to improve the actual solution. This algorithm is a straightforward generalization of Klafszky's and Terlaky's Hungarian Method. It has nearly the same structure as Ritter's algorithm (which is based on conjugate directions), but it does not use conjugate directions.     

A NEW GENERALIZED GRADIENT PROJECTION TYPEALGORITHM FOR LINEARLY CONSTRAINED PROBLEMS

In this paper, we provide a new generalized gradient projection algorithm for nonlinear programming problems with linear constraints. This algorithm has simple structure and is very practical and stable. Under the weaker assumptions, we have proved the global convergence of our algorithm.     

A penalty treatment of equality constraints in generalized geometric programming

A technique is described for solving generalized geometric programs whose constraints include one or more strict equalities. The algorithm solves a sequence of penalized geometric programs; the penalty functions are derived from the arithmetic-geometric inequality as condensed posynomials. Two examples serve to illustrate the idea.The authors appreciate the use of the program GGP provided by Professor R. S. Dembo.     

A statistical generalized programming algorithm for stochastic optimization problems

In this paper, we are concerned with an algorithm which combines the generalized linear programming technique proposed by Dantzig and Wolfe with the stochastic quasigradient method in order to solve stochastic programs with recourse. In this way, we overcome the difficulties arising in finding the exact values of the objective function of recourse problems by replacing them with the statistical estimates of the function. We present the basic steps of the proposed algorithm focusing our attention on its implementation alternatives aimed at improving both the convergence and computational performances. The main application areas are mentioned and some computational experience in the validation of our approach is reported. Finally, we discuss the possibilities of parallelization of the proposed algorithmic schemes.This paper has been partially supported by the Italian MURST 40% project on Flexible Manufacturing Systems.     

Trace optimization problems and generalized geometric programming

The approach of generalized geometric programming is extended to develop a dual version of trace optimization problems. As an application we present a new and simple derivation of the Massieu-Planck extremum principle for quantum statistical equilibrium ensembles.     

Fenchel's duality theorem in generalized geometric programming

Fenchel's duality theorem is extended to generalized geometric programming with explicit constraints—an extension that also generalizes and strengthens Slater's version of the Kuhn-Tucker theorem.This research was sponsored by the Air Force Office of Scientific Research, Air Force Systems Command, USAF, under Grant No. AFOSR-73-2516.     

A new polynomial-time algorithm for linear programming

We present a new polynomial-time algorithm for linear programming. In the worst case, the algorithm requiresO(n ^3.5 L) arithmetic operations onO(L) bit numbers, wheren is the number of variables andL is the number of bits in the input. The running-time of this algorithm is better than the ellipsoid algorithm by a factor ofO(n ^2.5). We prove that given a polytopeP and a strictly interior point a εP, there is a projective transformation of the space that mapsP, a toP′, a′ having the following property. The ratio of the radius of the smallest sphere with center a′, containingP′ to the radius of the largest sphere with center a′ contained inP′ isO(n). The algorithm consists of repeated application of such projective transformations each followed by optimization over an inscribed sphere to create a sequence of points which converges to the optimal solution in polynomial time. This is a substantially revised version of the paper presented at the Symposium on Theory of Computing, Washington D. C., April 1984.