Nonconvex quadratically constrained quadratic programming: best D.C. decompositions and their SDP representations

We propose in this paper a general D.C. decomposition scheme for constructing SDP relaxation formulations for a class of nonconvex quadratic programs with a nonconvex quadratic objective function and convex quadratic constraints. More specifically, we use rank-one matrices and constraint matrices to decompose the indefinite quadratic objective into a D.C. form and underestimate the concave terms in the D.C. decomposition formulation in order to get a convex relaxation of the original problem. We show that the best D.C. decomposition can be identified by solving an SDP problem. By suitably choosing the rank-one matrices and the linear underestimation, we are able to construct convex relaxations that dominate Shor’s SDP relaxation and the strengthened SDP relaxation. We then propose an extension of the D.C. decomposition to generate an SDP bound that is tighter than the SDP+RLT bound when additional box constraints are present. We demonstrate via computational results that the optimal D.C. decomposition schemes can generate both tight SDP bounds and feasible solutions with good approximation ratio for nonconvex quadratically constrained quadratic problems.     

Some remarks on the symmetric rank-one update

We consider the symmetric rank-one, quasi-Newton formula. The hereditary properties of this formula do not require quasi-Newton directions of search. Therefore, this formula is easy to use in constrained optimization algorithms; no explicit projections of either the Hessian approximations or the parameter changes are required. Moreover, the entire Hessian approximation is available at each iteration for determining the direction of search, which need not be a quasi-Newton direction. Theoretical difficulties, however, exist. Even for a positive-definite, quadratic function with no constraints, it is possible that the symmetric rank-one update may not be defined at some iteration. In this paper, we first demonstrate that such failures of definition correspond to either losses of independence in the directions of search being generated or to near-singularity of the Hessian approximation being generated. We then describe a procedure that guarantees that these updates are well-defined for any nonsingular quadratic function. This procedure has been incorporated into an algorithm for minimizing a function subject to box constraints. Box constraints arise naturally in the minimization of a function with many minima or a function that is defined only in some subregion of the space.     

On solving L-SR1 trust-region subproblems

In this article, we consider solvers for large-scale trust-region subproblems when the quadratic model is defined by a limited-memory symmetric rank-one (L-SR1) quasi-Newton matrix. We propose a solver that exploits the compact representation of L-SR1 matrices. Our approach makes use of both an orthonormal basis for the eigenspace of the L-SR1 matrix and the Sherman–Morrison–Woodbury formula to compute global solutions to trust-region subproblems. To compute the optimal Lagrange multiplier for the trust-region constraint, we use Newton’s method with a judicious initial guess that does not require safeguarding. A crucial property of this solver is that it is able to compute high-accuracy solutions even in the so-called hard case. Additionally, the optimal solution is determined directly by formula, not iteratively. Numerical experiments demonstrate the effectiveness of this solver.     

On the numerical stability of Huang's and related methods

This paper presents an analysis of Huang's and similar methods for solving systems of linear simultaneous equations, which not only derives their termination properties but which also permits bounds on propagated errors to be determined. The accuracy of Huang's method is shown to be proportional to the condition number of the matrix of coefficients of the equations. Finally, a class of methods having optimal stability characteristics is identified.The author is indebted to CNR for financial support while a Visiting Professor at the University of Bergamo, Bergamo, Italy.     

A RESTARTING DIRECTION FOR THE CONJUGATE GRADIENT METHOD

The main purpose of this paper is to provide a restarting direction for improving on the standard conjugate gradient method.If a drastic non-quadratic behaviour of the objective function is observed in the neighbour of xk,then a restart should be done.The scaling symmetric rank-one update with Davidon's optimal criterion is applied to generate the restarting direction.It is proved that the conjugate gradient method with this strategy retains the quadratic termination.Numerical experiments show that it is successful.     

Modified Partial-Update Newton-Type Algorithms for Unary Optimization

In this paper, we propose two modified partial-update algorithms for solving unconstrained unary optimization problems based on trust-region stabilization via indefinite dogleg curves. The two algorithms partially update an approximation to the Hessian matrix in each iteration by utilizing a number of times the rank-one updating of the Bunch–Parlett factorization. In contrast with the original algorithms in Ref. 1, the two algorithms not only converge globally, but possess also a locally quadratic or superlinear convergence rate. Furthermore, our numerical experiments show that the new algorithms outperform the trust-region method which uses the partial update criteria suggested in Ref. 1.     

Pivoting strategy for rank-one modification ofLDM t-like factorization

The rank-one modification algorithm of theLDM ^t factorization was given by Bennett [1]. His method, however, could break down even when the matrix is nonsingular and well-conditioned. We introduce a pivoting strategy for avoiding possible break-down as well as for suppressing error growth in the modification process. The method is based on a symbolic formula of the rank-one modification of the factorization of a possibly singular nonsymmetric matrix. A new symbolic formula is also obtained for the inverses of the factor matrices. Repeated application of our method produces theLDM ^t-like product form factorization of a matrix. A numerical example is given to illustrate our pivoting method. An incomplete factorization algorithm is also introduced for updating positive definite matrix useful in quasi-Newton methods, in which the Fletcher and Powell algorithm [2] and the Gill, Murray and Saunders algorithm [4] are usually used.This paper is presented at the Japan SIAM Annual Meeting held at University of Tokyo, Japan, October 7–9, 1991.     

Supermodularity and valid inequalities for quadratic optimization with indicators

Mathematical Programming - We study the minimization of a rank-one quadratic with indicators and show that the underlying set function obtained by projecting out the continuous variables is...     

Planar quasi-Newton algorithms for unconstrained saddlepoint problems

A new class of quasi-Newton methods is introduced that can locate a unique stationary point of ann-dimensional quadratic function in at mostn steps. When applied to positive-definite or negative-definite quadratic functions, the new class is identical to Huang's symmetric family of quasi-Newton methods (Ref. 1). Unlike the latter, however, the new family can handle indefinite quadratic forms and therefore is capable of solving saddlepoint problems that arise, for instance, in constrained optimization. The novel feature of the new class is a planar iteration that is activated whenever the algorithm encounters a near-singular direction of search, along which the objective function approaches zero curvature. In such iterations, the next point is selected as the stationary point of the objective function over a plane containing the problematic search direction, and the inverse Hessian approximation is updated with respect to that plane via a new four-parameter family of rank-three updates. It is shown that the new class possesses properties which are similar to or which generalize the properties of Huang's family. Furthermore, the new method is equivalent to Fletcher's (Ref. 2) modified version of Luenberger's (Ref. 3) hyperbolic pairs method, with respect to the metric defined by the initial inverse Hessian approximation. Several issues related to implementing the proposed method in nonquadratic cases are discussed.An earlier version of this paper was presented at the 10th Mathematical Programing Symposium, Montreal, Canada, 1979.     

Path Integrals in Noncommutative Quantum Mechanics

We consider an extension of the Feynman path integral to the quantum mechanics of noncommuting spatial coordinates and formulate the corresponding formalism for noncommutative classical dynamics related to quadratic Lagrangians (Hamiltonians). The basis of our approach is that a quantum mechanical system with a noncommutative configuration space can be regarded as another effective system with commuting spatial coordinates. Because the path integral for quadratic Lagrangians is exactly solvable and a general formula for the probability amplitude exists, we restrict our research to this class of Lagrangians. We find a general relation between quadratic Lagrangians in their commutative and noncommutative regimes and present the corresponding noncommutative path integral. This method is illustrated with two quantum mechanical systems in the noncommutative plane: a particle in a constant field and a harmonic oscillator.     

AN ITERATED-SUBSPACE MINIMIZATION METHODS WITH SYMMETRIC RANK-ONE UPDATING

We consider an Iterated-Subspace Minimization(ISM) method for solving large-scale unconstrained minimization problems.At each major iteration of the method, a two-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined, and a symmetric rank-one updating is used to solve the inner minimization problem.     

A Parallel Projection for the Multicommodity Network Model

We present an application of parallel computing techniques to the solution of a quadratic programme that arises in the resource-directive decomposition method for multicommodity problems. A sequential algorithm for the quadratic programme is discussed, and its extension to a parallel implementation is given. Computational testing of the sequential and parallel algorithms was done on the Sequent Symmetry S81 parallel computer located in the Parallel Processing Laboratory at Southern Methodist University. On several large test problems the parallel version achieved a speed-up of 10 with 12 processors.     

Optimal solutions for unrelated parallel machines scheduling problems using convex quadratic reformulations

In this work, we take advantage of the powerful quadratic programming theory to obtain optimal solutions of scheduling problems. We apply a methodology that starts, in contrast to more classical approaches, by formulating three unrelated parallel machine scheduling problems as 0–1 quadratic programs under linear constraints. By construction, these quadratic programs are non-convex. Therefore, before submitting them to a branch-and-bound procedure, we reformulate them in such a way that we can ensure convexity and a high-quality continuous lower bound. Experimental results show that this methodology is interesting by obtaining the best results in literature for two of the three studied scheduling problems.     

Integrable Systems and Rank-One Conditions for Rectangular Matrices

We give a determinantal formula for tau functions of the KP hierarchy in terms of rectangular constant matrices A, B, and C satisfying a rank-one condition. This result is shown to generalize and unify many previous results of different authors on constructions of tau functions for differential and difference integrable systems from square matrices satisfying rank-one conditions. In particular, its explicit special cases include Wilson's formula for tau functions of the rational KP solutions in terms of Calogero–Moser Lax matrices and our previous formula for the KP tau functions in terms of almost-intertwining matrices.     

The wave kernel for the Laplacian on the classical locally symmetric spaces of rank one,theta functions,trace formulas and the Selberg zeta function

We calculate the wave kernels for the classical rank-one symmetric spaces. The result is employed in order to provide a meromorphic extension of the theta function of an even-dimensional compact locally symmetric space of non-compact type. Moreover we give a short derivation of the Selberg trace formula. We discuss the relation between the right hand side of the functional equation of the Selberg zeta function, the Plancherel measure, Weyl's dimension formula and the wave kernel on the non-compact symmetric space and on its compact dual in an explicit manner.The first two authors were supported by the Sonderforschungsbereich 288 Differentialgeometrie und Quantenphysik founded by the Deutsche Forschungsgemeinschaft.     

New semidefinite programming relaxations for box constrained quadratic program

We establish in this paper optimal parametric Lagrangian dual models for box constrained quadratic program based on the generalized D.C. (difference between convex) optimization approach, which can be reformulated as semidefinite programming problems. As an application, we propose new valid linear constraints for rank-one relaxation.     

The Number of Singular Vector Tuples and Uniqueness of Best Rank-One Approximation of Tensors

In this paper we discuss the notion of singular vector tuples of a complex-valued \(d\) -mode tensor of dimension \(m_1\times \cdots \times m_d\) . We show that a generic tensor has a finite number of singular vector tuples, viewed as points in the corresponding Segre product. We give the formula for the number of singular vector tuples. We show similar results for tensors with partial symmetry. We give analogous results for the homogeneous pencil eigenvalue problem for cubic tensors, i.e., \(m_1=\cdots =m_d\) . We show the uniqueness of best approximations for almost all real tensors in the following cases: rank-one approximation; rank-one approximation for partially symmetric tensors (this approximation is also partially symmetric); rank- \((r_1,\ldots ,r_d)\) approximation for \(d\) -mode tensors.     

Modifying the generalized singular value decomposition with application with application in direction-of-arrival finding

We consider updating and downdating problems for the generalized singular value decomposition (GSVD) of matrix pairs when new rows are added to one of the matrices or old rows are deleted. Two classes of algorithms are developed, one based on the CS decomposition formulation of the GSVD and the other based on the generalized eigenvalue decomposition formulation. In both cases we show that the updating and downdating problems can be reduced to a rank-one SVD updating problem. We also provide perturbation analysis for the cases when the added or deleted rows are subject to errors. Numerical experiments on direction-of-arrival (DOA) finding with colored noises are carried out to demonstrate the tracking ability of the algorithms. The work of the first author was supported in part by NSF grants CCR-9308399 and CCR-9619452. The work of the second author was supported in part by China State Major Key Project for Basic Researches.     

一个修正的秩1拟牛顿法及其收敛性质

的拟牛顿算法,具有结构简单,易于实现的特点.当用于正定二次凸函数时,算法有较好的收敛性质.但是,它也有严重的缺点.一方面,即使 H_k 正定,也不能保证 H_(k+1) 是正定的;另一方面,(S_k—H_kYk)~TY_k 可能为0,这时算法就不再有定义.自从秩1拟牛顿法问世以来,许多学者都想将其改变为一个有用的算法(参见),