Composite geometric programming

Geometric programming is based on functions called posynomials, the terms of which are log-linear. This class of programs is extended from the composition of an exponential and a linear function to an exponential and a convex function. The resulting duality theory for composite geometric programs retains many of the qualities of geometric programming duality, while at the same time encompassing new areas of application. As an application, composite geometric programming is applied to exponential geometric programming. A pure dual is developed for the first time and used to solve a problem from the literature.This research was supported by the Air Force Office of Scientific Research, Grant No. AFOSR-83-0234.     

A note on duality in disjunctive programming

We state a duality theorem for disjunctive programming, which generalizes to this class of problems the corresponding result for linear programming.This work was supported by the National Science Foundation under Grant No. MPS73-08534 A02 and by the US Office of Naval Research under Contract No. N00014-75-C-0621-NR047-048.     

Solving geometric programs using GRG: Results and comparisons

This paper describes the performance of a general-purpose GRG code for nonlinear programming in solving geometric programs. The main conclusions drawn from the experiments reported are: (i) GRG competes well with special-purpose geometric programming codes in solving geometric programs; and (ii) standard time, as defined by Colville, is an inadequate means of compensating for different computing environments while comparing optimization algorithms.This research was partially supported by the Office of Naval Research under Contracts Nos. N00014-75-C-0267 and N00014-75-C-0865, the US Energy Research and Development Administration, Contract No. E(04-3)-326 PA-18, and the National Science Foundation, Grant No. DCR75-04544 at Stanford University; and by the Office of Naval Research under Contract No. N00014-75-C-0240, and the National Science Foundation, Grant No. SOC74-23808, at Case Western Reserve University.     

Algorithms for a class of nondifferentiable problems

A nonlinear programming problem with nondifferentiabilities is considered. The nondifferentiabilities are due to terms of the form min(f ₁(x),...,f _n(x)), which may enter nonlinearly in the cost and the constraints. Necessary and sufficient conditions are developed. Two algorithms for solving this problem are described, and their convergence is studied. A duality framework for interpretation of the algorithms is also developed.This work was supported in part by the National Science Foundation under Grant No. ENG-74-19332 and Grant No. ECS-79-19396, in part by the U.S. Air Force under Grant AFOSR-78-3633, and in part by the Joint Services Electronics Program (U.S. Army, U.S. Navy, and U.S. Air Force) under Contract N00014-79-C-0424.     

Strong duality for a special class of integer programs

A pair of primal-dual integer programs is constructed for a class of problems motivated by a generalization of the concept of greatest common divisor. The primal-dual formulation is based on a number-theoretic, rather than a Lagrangian, duality property; consequently, it avoids the dualitygap common to Lagrangian duals in integer programming.This research was partially supported by the National Science Foundation, Grant No. DCR-74-20584     

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 comparison of computational strategies for geometric programs

Numerous algorithms for the solution of geometric programs have been reported in the literature. Nearly all are based on the use of conventional programming techniques specialized to exploit the characteristic structure of either the primal or the dual or a transformed primal problem. This paper attempts to elucidate, via computational comparisons, whether a primal, a dual, or a transformed primal solution approach is to be preferred.The authors wish to thank Captain P. A. Beck and Dr. R. S. Dembo for making available their codes. This research was supported in part under ONR Contract No. N00014-76-C-0551 with Purdue University.     

Algorithmic equivalence in quadratic programming,I: A least-distance programming problem

It is demonstrated that Wolfe's algorithm for finding the point of smallest Euclidean norm in a given convex polytope generates the same sequence of feasible points as does the van de Panne-Whinstonsymmetric algorithm applied to the associated quadratic programming problem. Furthermore, it is shown how the latter algorithm may be simplified for application to problems of this type.This work was supported by the National Science Foundation, Grant No. MCS-71-03341-AO4, and by the Office of Naval Research, Contract No. N00014-75-C-0267.     

Optimal simplex tableau characterization of unique and bounded solutions of linear programs

Uniqueness and boundedness of solutions of linear programs are characterized in terms of an optimal simplex tableau. LetM denote the submatrix in an optimal simplex tableau with columns corresponding to degenerate optimal dual basic variables. A primal optimal solution is unique iff there exists a nonvacuous nonnegative linear combination of the rows ofM, corresponding to degenerate optimal primal basic variables, which is positive. The set of primal optimal solutions is bounded iff there exists a nonnegative linear combination of the rows ofM which is positive. WhenM is empty, the primal optimal solution is unique.This research was sponsored by the United States Army under Contract No. DAAG29-75-C-0024. This material is based upon work supported by the National Science Foundation under Grant No. MCS-79-01066.     

A strong duality theorem for the minimum of a family of convex programs

We produce a duality theorem for the minimum of an arbitrary family of convex programs. This duality theorem provides a single concave dual maximization and generalizes recent work in linear disjunctive programming. Homogeneous and symmetric formulations are studied in some detail, and a number of convex and nonconvex applications are given.This work was partially funded by National Research Council of Canada, Grant No. A4493. Thanks are due to Mr. B. Toulany for many conversations and to Dr. L. MacLean who suggested the chance-constrained model.     

A second-order method for the general nonlinear programming problem

This paper presents a multiplier-type method for nonlinear programming problems with both equality and inequality constraints. Slack variables are used for the inequalities. The penalty coefficient is adjusted automatically, and the method converges quadratically to points satisfying second-order conditions.The work of the first author was supported by NSF RANN and JSEP Contract No. F44620-71-C-0087; the work of the second author was supported by the National Science Foundation Grant No. ENG73-08214A01 and US Army Research Office Durham Contract No. DAHC04-73-C-0025.     

Posynomial geometric programming as a special case of semi-infinite linear programming

This paper develops a wholly linear formulation of the posynomial geometric programming problem. It is shown that the primal geometric programming problem is equivalent to a semi-infinite linear program, and the dual problem is equivalent to a generalized linear program. Furthermore, the duality results that are available for the traditionally defined primal-dual pair are readily obtained from the duality theory for semi-infinite linear programs. It is also shown that two efficient algorithms (one primal based and the other dual based) for geometric programming actually operate on the semi-infinite linear program and its dual.     

Projection and restriction methods in geometric programming and related problems

Mathematical programming problems with unattained infima or unbounded optimal solution sets are dual to problems which lackinterior points, e.g., problems for which the Slater condition fails to hold or for which the hypothesis of Fenchel's theorem fails to hold. In such cases, it is possible to project the unbounded problem onto a subspace and to restrict the dual problem to an affine set so that the infima are not altered. After a finite sequence of such projections and restrictions, dual problems are obtained which have bounded optimal solution sets andinterior points. Although results of this kind have occasionally been used in other contexts, it is in geometric programming (both in the original psynomial form and the generalized form) where such methods appear most useful. In this paper, we present a treatment of dual projection and restriction methods developed in terms of dual generalized geometric programming problems. Analogous results are given for Fenchel and ordinary dual problems.This research was supported in part by Grant No. AFOSR-73-2516 from the Air Force Office of Scientific Research and by Grant No. NSF-ENG-76-10260 from the National Science Foundation.The authors wish to express their appreciation to the referees for several helpful comments.     

Linearly constrained convex programming as unconstrained differentiable concave programming

Recently, Fang proposed approximating a linear program in Karmarkar's standard form by adding an entropic barrier function to the objective function and using a certain geometric inequality to transform the resulting problem into an unconstrained differentiable concave program. We show that, by using standard duality theory for convex programming, the results of Fang and his coworkers can be strengthened and extended to linearly constrained convex programs and more general barrier functions.This research was supported by the National Science Foundation, Grant No. CCR-91-03804.     

Characterization of optimality in convex programming without a constraint qualification

Necessary and sufficient conditions of optimality are given for convex programming problems with no constraint qualification. The optimality conditions are stated in terms of consistency or inconsistency of a family of systems of linear inequalities and cone relations.This research was supported by Project No. NR-047-021, ONR Contract No. N00014-67-A-0126-0009 with the Center for Cybernetics Studies, The University of Texas; by NSF Grant No. ENG-76-10260 at Northwestern University; and by the National Research Council of Canada.     

Exact penalty functions in nonlinear programming

It is shown that the existence of a strict local minimum satisfying the constraint qualification of [16] or McCormick's [12] second order sufficient optimality condition implies the existence of a class of exact local penalty functions (that is ones with a finite value of the penalty parameter) for a nonlinear programming problem. A lower bound to the penalty parameter is given by a norm of the optimal Lagrange multipliers which is dual to the norm used in the penalty function.Sponsored by the United States Army under Contract No. DAAG29-75-C-0024 and by the National Science Foundation under Grant No. MCS74-20584 A02.     

Optimization Techniques for State-Constrained Control and Obstacle Problems

The design of control laws for systems subject to complex state constraints still presents a significant challenge. This paper explores a dynamic programming approach to a specific class of such problems, that of reachability under state constraints. The problems are formulated in terms of nonstandard minmax and maxmin cost functionals, and the corresponding value functions are given in terms of Hamilton-Jacobi-Bellman (HJB) equations or variational inequalities. The solution of these relations is complicated in general; however, for linear systems, the value functions may be described also in terms of duality relations of convex analysis and minmax theory. Consequently, solution techniques specific to systems with a linear structure may be designed independently of HJB theory. These techniques are illustrated through two examples.The first author was supported by the Russian Foundation for Basic Research, Grant 03-01-00663, by the program Universities of Russia, Grant 03.03.007, and by the program of the Russian Federation President for the support of scientific research in leading scientific schools, Grant NSh-1889.2003.1.The second author was supported by the National Science and Engineering Research Council of Canada and by ONR MURI Contract 79846-23800-44-NDSAS.The third and first authors were supported by NSF Grants ECS-0099824 and ECS-0424445.Communicated by G. Leitmann     

Duality in infinite dimensional linear programming

We consider the class of linear programs with infinitely many variables and constraints having the property that every constraint contains at most finitely many variables while every variable appears in at most finitely many constraints. Examples include production planning and equipment replacement over an infinite horizon. We form the natural dual linear programming problem and prove strong duality under a transversality condition that dual prices are asymptotically zero. That is, we show, under this transversality condition, that optimal solutions are attained in both primal and dual problems and their optimal values are equal. The transversality condition, and hence strong duality, is established for an infinite horizon production planning problem.This material is based on work supported by the National Science Foundation under Grant No. ECS-8700836.     

Optimal design of pitched laminated wood beams

The optimal design of a pitched laminated wood beam is considered. An engineering formulation is given in which the volume of the beam is minimized. The problem is then reformulated and solved as a generalized geometric (signomial) program. Sample designs are presented.This research was partially supported by the Office of Naval Research under Contracts Nos. N00014-75-C-0267 and N00014-75-C-0865; by the US Energy Research and Development Administration Contract No. E(04-3)-326 PA-18; and by the National Science Foundation, Grant No. DCR75-04544 at Stanford University. This work was carried out during the first author's stay at the Management Science Division of the University of British Columbia and the Systems Optimization Laboratory of Stanford University. The authors are indebted to Mr. S. Liu and Mrs. M. Ratner for their assistance in performing the computations.     

A note on an open problem in linear complementarity

LetK be the class ofn × n matricesM such that for everyn-vectorq for which the linear complementarity problem (q, M) is feasible, then the problem (q, M) has a solution. Recently, a characterization ofK has been obtained by Mangasarian [5] in his study of solving linear complementarity problems as linear programs. This note proves a result which improves on such a characterization.Research sponsored by the United States Army under Contract No. DAAG29-75-C-0024 and the National Science Foundation under Grant No. MCS75-17385.