Schwarz alternating and iterative refinement methods for mixed formulations of elliptic problems,part II: Convergence theory

Summary In this paper we discuss bounds for the convergence rates of several domain decomposition algorithms to solve symmetric, indefinite linear systems arising from mixed finite element discretizations of elliptic problems. The algorithms include Schwarz methods and iterative refinement methods on locally refined grids. The implementation of Schwarz and iterative refinement algorithms have been discussed in part I. A discussion on the stability of mixed discretizations on locally refined grids is included and quantiative estimates for the convergence rates of some iterative refinement algorithms are also derived.Department of Mathematics, University of Wyoming, Laramie, WY 82071-3036. This work was supported in part by the National Science Foundation under Grant NSF-CCR-8903003, while the author was a graduate student at New York University, and in part by NSF Grant ASC 9003002, while the author was a Visiting, Assistant Researcher at UCLA.     

Modified predictor-corrector algorithm for locating weighted centers in linear programming

In certain applications of linear programming, the determination of a particular solution, the weighted center of the solution set, is often desired, giving rise to the need for algorithms capable of locating such center. In this paper, we modify the Mizuno-Todd-Ye predictor-corrector algorithm so that the modified algorithm is guaranteed to converge to the weighted center for given weights. The key idea is to ensure that iterates remain in a sequence of shrinking neighborhoods of the weighted central path. The modified algorithm also possesses polynomiality and superlinear convergence.The work of the first author was supported in part by NSF Grant DMS-91-02761 and DOE Contract DE-FG05-91-ER25100.The work of the second author was supported in part by NSF Cooperative Agreement CCR-88-09615.     

Second order parallel algorithms for piecewise smooth displacement kernels

Second order parallel algorithms for Fredholm integral equations with piecewise smooth displacement kernels are derived. One is based on a difference scheme of Runge-Kutta type for an unusual partial differential equations for continuous functions of two variables. The other is based on the trapezoidal quadrature rule applied to a modified integral equations. It is found that the Runge-Kutta type algorithm exhibits certain advantages.The work of these authors was supported in part by the NSF Grant DMS-9007030The work of this author was supported in part by a grant from the National Science and Engineering Research Council of Canada     

On the need for special purpose algorithms for minimax eigenvalue problems

It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimization problems involving smooth objective and constraint functions. This result seems very appealing since minimax eigenvalue problems are known to be typically nondifferentiable. In this paper, we show, however, that general purpose nonlinear optimization algorithms usually fail to find a solution to these smooth problems even in the simple case of minimization of the maximum eigenvalue of an affine family of symmetric matrices, a convex problem for which efficient algorithms are available.This work was supported in part by NSF Engineering Research Centers Program No. NSFD-CDR-88-03012 and NSF Grant DMC-84-20740. The author wishes to thank Drs. M. K. H. Fan and A. L. Tits for their many useful suggestions.     

A general parametric analysis approach and its implication to sensitivity analysis in interior point methods

Adler and Monteiro (1992) developed a parametric analysis approach that is naturally related to the geometry of the linear program. This approach is based on the availability of primal and dual optimal solutions satisfying strong complementarity. In this paper, we develop an alternative geometric approach for parametric analysis which does not require the strong complementarity condition. This parametric analysis approach is used to develop range and marginal analysis techniques which are suitable for interior point methods. Two approaches are developed, namely the LU factorization approach and the affine scaling approach. Presented at the ORSA/TIMS, Nashville, TN, USA, May 1991. Supported by the National Science Foundation (NSF) under Grant No. DDM-9109404 and Grant No. DMI-9496178. This work was done while the author was a faculty member of the Systems and Industrial Engineering Department at The University of Arizona. Supported in part by the GTE Laboratories and the National Science Foundation (NSF) under Grant No. CCR-9019469.     

Superlinear and quadratic convergence of primal-dual interior-point methods for linear programming revisited

Recently, Zhang, Tapia, and Dennis (Ref. 1) produced a superlinear and quadratic convergence theory for the duality gap sequence in primal-dual interior-point methods for linear programming. In this theory, a basic assumption for superlinear convergence is the convergence of the iteration sequence; and a basic assumption for quadratic convergence is nondegeneracy. Several recent research projects have either used or built on this theory under one or both of the above-mentioned assumptions. In this paper, we remove both assumptions from the Zhang-Tapia-Dennis theory.Dedicated to the Memory of Magnus R. Hestenes, 1906–1991This research was supported in part by NSF Cooperative Agreement CCR-88-09615 and was initiated while the first author was at Rice University as a Visiting Member of the Center for Research in Parallel Computation.The authors thank Yinyu Ye for constructive comments and discussions concerning this material.This author was supported in part by NSF Grant DMS-91-02761 and DOE Grant DE-FG05-91-ER25100.This author was supported in part by AFOSR Grant 89-0363, DOE Grant DE-FG05-86-ER25017, and ARO Grant 9DAAL03-90-G-0093.     

New decomposition and convexification algorithm for nonconvex large-scale primal-dual optimization

A new algorithm for solving nonconvex, equality-constrained optimization problems with separable structures is proposed in the present paper. A new augmented Lagrangian function is derived, and an iterative method is presented. The new proposed Lagrangian function preserves separability when the original problem is separable, and the property of linear convergence of the new algorithm is also presented. Unlike earlier algorithms for nonconvex decomposition, the convergence ratio for this method can be made arbitrarily small. Furthermore, it is feasible to extend this method to algorithms suited for inequality-constrained optimization problems. An example is included to illustrate the method.This research was supported in part by the National Science Foundation under NSF Grant No. ECS-85-06249.     

On the perturbation analysis of discrete-event dynamic systems

The paper describes a new approach to the analysis and optimization of discrete-event dynamic systems, such as queueing networks.Dedicated to G. LeitmannThe work reported in this paper was supported in part by ONR Contracts Nos. N00014-75-C-0648 and N00014-79-C-0776 and in part by NSF Grant No. NSF-ECS-82-13680.     

Constant potential primal—dual algorithms: A framework

We start with a study of the primal—dual affine-scaling algorithms for linear programs. Using ideas from Kojima et al., Mizuno and Nagasawa, and new potential functions we establish a framework for primal—dual algorithms that keep a potential function value fixed. We show that if the potential function used in the algorithm is compatible with a corresponding neighborhood of the central path then the convergence proofs simplify greatly. Our algorithms have the property that all the iterates can be kept in a neighborhood of the central path without using any centering in the search directions.Research performed while the author was Ph.D. student at Cornell University and was supported in part by the United States Army Research Office through the Army Center of Excellence for Symbolic Methods in Algorithmic Mathematics (ACSyAM), Mathematical Sciences Institute of Cornell University, Contract DAAL03-91-C-0027, and also by NSF, AFOSR and ONR through NSF Grant DMS-8920550.     

The block decomposition of a vandermonde matrix and its applications

This paper presents a new algorithm for the solution of linear equations with a Vandermonde coefficient matrix. The algorithm can also be used to solve the dual problem. Since the algorithm uses a block decomposition of the matrix, it is especially suitable for parallel computation. A variation of the block decomposition leads to the efficient solution of the interpolation problem with complex-conjugate interpolation points where the coefficients of the interpolating polynomial are real. In addition the algorithm can be used to solve some kinds of confluent Vandermonde systems.W. P. Tang is a graduate student in the Computer Science Department at Stanford University, on leave from the Institute of Computing Technology of the Chinese Academy of Science in Peking.The work of Professor Golub was in part supported by NSF Grant No. MCS78-11985.     

A second-order method for unconstrained optimization

This paper presents a quadratically converging algorithm for unconstrained minimization. All the accumulation points that it constructs satisfy second-order necessary conditions of optimality. Thus, it avoids second-order saddle andinflection points, an essential feature for a method to be used in minimizing the modified Lagrangians in multiplier methods.The work of the first author was supported by NSF RANN AEN 73-07732-A02 and JSEP Contract No. F44620-71-C-0087; the work of the second author was supported by NSF Grant No. GK-37672 and the ARO Contract No. DAHCO4-730C-0025.     

Implementing proximal point methods for linear programming

We describe the application of proximal point methods to the linear programming problem. Two basic methods are discussed. The first, which has been investigated by Mangasarian and others, is essentially the well-known method of multipliers. This approach gives rise at each iteration to a weakly convex quadratic program which may be solved inexactly using a point-SOR technique. The second approach is based on the proximal method of multipliers, originally proposed by Rockafellar, for which the quadratic program at each iteration is strongly convex. A number of techniques are used to solve this subproblem, the most promising of which appears to be a two-metric gradient-projection approach. Convergence results are given, and some numerical experience is reported.This research was supported by National Science Foundation, Grant Nos. ASC-87-14009 and DMS-86-19903, and by Air Force Office of Scientific Research, Grant No. AFOSR-ISSA-87-0092. Part of this work was performed at Argonne National Laboratory. Computation was partly performed at the Pittsburgh Supercomputer Center under NSF Grant No. DMS-88-0033P and at the Argonne Advanced Computing Research Facility, whose support is gratefully acknowledged. The author thanks Olvi Mangasarian and Renato DeLeone for helpful discussions, and Jorge Moré for his valuable advice on the algorithms discussed in Section 3. The contributions of a referee, who provided helpful comments on an earlier version of this paper, are also acknowledged.     

On the Stackelberg strategy in nonzero-sum games

The properties of the Stackelberg solution in static and dynamic nonzero-sum two-player games are investigated, and necessary and sufficient conditions for its existence are derived. Several game problems, such as games where one of the two players does not know the other's performance criterion or games with different speeds in computing the strategies, are best modeled and solved within this solution concept. In the case of dynamic games, linear-quadratic problems are formulated and solved in a Hilbert space setting. As a special case, nonzero-sum linear-quadratic differential games are treated in detail, and the open-loop Stackelberg solution is obtained in terms of Riccati-like matrix differential equations. The results are applied to a simple nonzero-sum pursuit-evasion problem.This work was supported in part by the US Air Force under Grant No. AFOSR-68-1579D, in part by NSF under Grant No. GK-36276, and in part by the Joint Services Electronics Program under Contract No. DAAB-07-72-C-0259 with the Coordinated Science Laboratory, University of Illinois, Urbana, Illinois.     

Shortest paths algorithms: Theory and experimental evaluation

We conduct an extensive computational study of shortest paths algorithms, including some very recent algorithms. We also suggest new algorithms motivated by the experimental results and prove interesting theoretical results suggested by the experimental data. Our computational study is based on several natural problem classes which identify strengths and weaknesses of various algorithms. These problem classes and algorithm implementations form an environment for testing the performance of shortest paths algorithms. The interaction between the experimental evaluation of algorithm behavior and the theoretical analysis of algorithm performance plays an important role in our research. This work was done while Boris V. Cherkassky was visiting Stanford University Computer Science Department and supported by the NSF and Powell Foundation grants mentioned below. Andrew V. Goldberg was supported in part by ONR Young Investigator Award N00014-91-J-1855, NSF Presidential Young Investigator Grant CCR-8858097 with matching funds from AT&T, DEC, and 3M, and a grant from Powell Foundation. Corresponding author. This work was done while Tomasz Radzik was a Postdoctoral Fellow at SORIE, Cornell University, and supported by the National Science Foundation, the Air Force Office of Scientific Research, and the Office of Naval Research, through NSF grant DMS-8920550, and by the Packard Fellowship of éva Tardos.     

Geometric rigidity of transcendental meromorphic functions

The classes of dynamically and geometrically tame functions meromorphic outside a small set are introduced. The Julia sets of geometrically tame functions are proven to be either geometrical circle (in ) or to have Hausdorff dimension strictly larger than 1. Vast classes of dynamically and geometrically tame functions are identified. The research of both authors was supported in part by the Polish KBN Grant No 2 PO3A 034 25, the Warsaw University of Technology Grant no 504G 11200043000 and by the NSF/PAN grant INT-0306004. The research of the second author was supported in part by the NSF Grant DMS 0400481.     

The translation representation theorem

Short of a new theorem on semigroups of operators, a new proof of an old theorem on this subject is a suitable offering to Einar Hille on his 85th birthday.The work of both authors was supported in part by the National Science Foundation, the first author under Grant No. MCS-76-07039 and the second author under Grant No. MCS-77-04908 A 01.     

An operator-theoretic approach to theorems of the Pick-Nevanlinna and Loewner types. I

In this paper we formulate a general operator-theoretic result from which we deduce some old and some new function-theoretic interpolation theorems. The theorems we are concerned with have as prototype the Pick-Nevanlinna theorem and the Loewner theorem on restrictions of functions with positive imaginary part in the upper half plane.This work was supported in part by NSF Grant MCS 78-00408. The second author also acknowledges support from the Alexander von Humboldt Foundation, and he thanks the Rheinisch-Westfälisch Technische Hochschule Aachen for its hospitality at the time when this work was completed.     

Local spectral properties of multipliers on Banach algebras

Supported in part by NSF Grant DMS 90-96108. This work was completed while the first author visited Mississippi State University. Financial support and generous hospitality are gratefully acknowledged.     

Bounds on the size of tetrahedralizations

This paper gives lower and upper bounds on the complexity of triangulating the region between polyhedra. Particular attention is given to the case of convex polyhedra and terrains. The first author was suported in part by NSF Grant CCR-90-02352 and The Geometry Center, University of Minnesota, an STC funded by NSF, DOE, and Minnesota Technology, Inc. The second author was supported in part by NSF Grant PHY-90-21984.     

Linear stochastic differential equations with boundary conditions

Summary We study linear stochastic differential equations with affine boundary conditions. The equation is linear in the sense that both the drift and the diffusion coefficient are affine functions of the solution. The solution is not adapted to the driving Brownian motion, and we use the extended stochastic calculus of Nualart and Pardoux [16] to analyse them. We give analytical necessary and sufficient conditions for existence and uniqueness of a solution, we establish sufficient conditions for the existence of probability densities using both the Malliavin calculus and the co-aera formula, and give sufficient conditions that the solution be either a Markov process or a Markov field.Supported in part by NSF Grant No. MCS-8301880The research was carried out while this author was visiting the Institute for Advanced Study, Princeton NJ, and was supported by a grant from the RCA corporation