**Optimization of a Constrained Quadratic Function**

**Charles HAMAKER**

Department of Mathematics and Computer Science

PO Box 3517 Saint Mary’s College of California,

Moraga, CA,94575

chamaker@stmarys-ca.edu

**Dedicated on the occasion of his 60th birthday to Neculai Andrei in appreciation of a lifetime of contributions to mathematics, as a productive researcher, an author of valuable texts and software, and a leader in the mathematical community.**

**Abstract**: For *A* a positive definite, symmetric *n* x* n* matrix and b a real *n*-vector, the objective function is optimized over the unit sphere. The proposed iterative methods, based on the gradient of *f*, converge in general for maximization and for large |b| for minimization with the principal computational cost being one or two matrix-vector multiplications per iteration. The rate of convergence improves as |b| increases, becoming computationally competitive in that case with algorithms developed for the more general problem wherein *A* may be indefinite.

**Keywords**: Constrained optimization, quadratic functions, iterated gradients, acceleration of convergence.

**Charles Hamaker** works in applicable analysis, particularly the application of Radon transforms to mathematical tomography and differential equations. He is also the director of his college’s core undergraduate program in reading classical texts of western culture.

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**CITE THIS PAPER AS**:

Charles HAMAKER, **Optimization of a Constrained Quadratic Function**, *Studies in Informatics and Control*, ISSN 1220-1766, vol. 18 (1), pp. 21-32, 2009.

**1. Introduction and Preliminaries
**

Let *A* be a real-valued, positive definite, symmetric *n* x *n* matrix, and let **b** be a real-valued *n*-vector. The problem under consideration here is:

For small dimensions, the well-established approach involving diagonalization of *A*, outlined in [Golub and Van Loan 1996] and sketched in the remark preceding Lemma 1, suffices for both maximization and minimization. For large dimensions, the computational cost of diagonalization is excessive.

The maximization problem was encountered for *n*=4 in computations for lattice gauge simulations [Montero, 1999]. With *A* allowed to be indefinite, the minimization problem is the trust region subproblem which must be solved in each iteration of a trust region method [Conn, Gould, Toint, 2000] and [Hager, 2001]. The subproblem has been the subject of considerable attention, resulting in sophisticated iterative algorithms (see [Hager, 2001], [Sorenson, 1997], and [Rendl and Wolkowicz, 1997]). For instance, the SSM algorithm described in [Hager, 2001] converges quadratically, using the Lanczos process for startup and with each iterate being the solution of the problem on a low-dimensional subspace. That subspace is determined in part by applying Newton’s method at the previous iterate to the associated Lagrange problem. As is typical of these algorithms, the tradeoff for the small number of iterations required is that initialization and each iteration require a significant number of matrix-vector multiplications. See [Hager, 2001] for a comparison of the computational effectiveness of some of these algorithms. These minimization algorithms are adaptable to the maximization problem presumably with similar computational cost.

Denoting the eigenvalues of *A* by , direct the corresponding orthonormal basis of eigenvectors so that

and in the case that , so that

and let *E* denote the orthogonal matrix having the as columns. Then, since the change of coordinates diagonalizes *A* and preserves the constraint set , it can be observed that

Thus, with respect to the coordinates , the problem takes the diagonalized form:

Optimize over where and are as above.

It is convenient to adopt the following notations. For , representation in coordinates will be with respect to the orthonormal eigenbasis and will be denoted by . The outer subscript will be omitted when no ambiguity results, e.g. while . The euclidean norm of a vector **x** will be denoted by |**x**|. The subset of consisting of all vectors with non-negative (non-positive) coordinates will be denoted by .

Geometric insight into the problem follows from completing the square in the diagonalized form of to obtain

where

Thus, for *c*>-*k*, the level hypersurface *f*(**x**) = *c* is an ellipsoid with center at .

It is the simplicity of this geometric characterization that suggests the iterative algorithm for the maximization problem: where **T**(**x**) is the normalization of the gradient. Note that the solution is a fixed point for **T**. While convergence in general will be established, it is reasonable to expect that the rate will improve as |**b**| increases, moving the common center of the ellipsoids further from the origin and thus resulting in less variation of over the constraint set . The algorithm for the minimization problem (See section 4) is based on similar heuristic considerations.

Characterization of the maximizing vector can begin by observing in the diagonalized form of the problem that it must have non-negative coordinates because the are positive, and furthermore must satisfy the Lagrange multiplier condition:

for some (1)

Since , this implies

(2)

This leads to defining

and observing that the requirement that means that μ must satisfy the secular equation g(t)=1.

If b_{1}>0, then the requirement that implies that , and hence that all the are defined by (2). Because g(t) is unbounded and decreasing on and approaches 0 as , there exists a unique satisfying . Thus there is a unique maximizing point w.

In the case that and , the requirement that implies , and there is a unique satisfying . Then (2) can be satisfied for *i < k *in one of two ways. First, if , then . Second, if and (in which case ) the unit vectors determined by the Lagrange condition have coordinates determined by (2) for , while . Thus , where

For the unit vector determined by in (2),, and is positive for . Thus the desired maximum corresponds to the largest value of μ satisfying (1), namely the larger of and .

**Remark:** Assuming that the problem has been diagonalized, i.e. the eigenvectors have been found, and that , *μ* in equation (1) can be found by solving the secular equation g(t)=1 and then used in equation (2) to find w. This approach is outlined in [Golub and Van Loan, 1996] for the minimization problem and used in [Montero, 1999] to solve the maximization problem for *n = *4.

The resulting characterization of the maximizing vector is summarized as follows.

**Lemma 1 **

*1. If , then the maximizing vector w satisfies and the corresponding Lagrange multiplier satisfies . *

*2. If and , then and . *

*3. If and , then with the set S of maximizing vectors consisting of all w with determined by (2) for and satisfying *.

Characterization of the minimizing vector v as corresponding via (2) to the smallest value which satisfies (1) is completely similar, and is given in Lemma 2.2 of [Hager 2001]. In that paper, the counterparts to cases 1, 2, and 3. are referred to as *non-degenerate, non-degenerate degenerate*, and *degenerate*, and the literature on the trust region subproblem refers to cases 2 and 3 collectively as the *hard case*.

It is useful to characterize the optimizing vectors and their multipliers for |**b**| large.

**Lemma 2** *For , let b= τ u, τ > 0. Then, for fixed A, *

*1. and . *

*2. and *

Proof: The secular equation *g(t) = *1 becomes

To establish case 1, note first that *µ*_{o}, the largest solution of the secular equation, satisfies . Thus for τ large enough,, and so . Setting *t = μ* in the secular equation, multiplying by , and taking limits gives

Then

Case 2 is established similarly.

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