Functions Hessian matrix

Xk) is the inverse Hessian matrix of second derivatives, which, in the Newton-Raphson method, must therefore be inverted. This cem be computationally demanding for systems u ith many atoms and can also require a significant amount of storage. The Newton-Uaphson method is thus more suited to small molecules (usually less than 100 atoms or so). For a purely quadratic function the Newton-Raphson method finds the rniriimum in one step from any point on the surface, as we will now show for our function f x,y) =x + 2/. 

The eigenvalues (coa of the mass weighted Hessian matrix (see below) are used to compute, for each of the 3N-7 vibrations with real and positive cOa values, a vibrational partition function that is combined to produce a transition-state vibrational partition function ... 

Order 2 minimization algorithms, which use the second derivative (curvamre) as well as the first derivative (slope) of the potential function, exhibit in many cases improved rate of convergence. For a molecule of N atoms these methods require calculating the 3N X 3N Hessian matrix of second derivatives (for the coordinate set at step k)... 

We are now able to obtain the Hessian matrix of the objective function S(k) which is denoted by H and is given by the following equation... 

These methods utilize only values of the objective function, S(k), and values of the first derivatives of the objective function. Thus, they avoid calculation of the elements of the (pxp) Hessian matrix. The quasi-Newton methods rely on formulas that approximate the Hessian and its inverse. Two algorithms have been developed ... 

Instead of a formal development of conditions that define a local optimum, we present a more intuitive kinematic illustration. Consider the contour plot of the objective function fix), given in Fig. 3-54, as a smooth valley in space of the variables X and x2. For the contour plot of this unconstrained problem Min/(x), consider a ball rolling in this valley to the lowest point offix), denoted by x. This point is at least a local minimum and is defined by a point with a zero gradient and at least nonnegative curvature in all (nonzero) directions p. We use the first-derivative (gradient) vector Vf(x) and second-derivative (Hessian) matrix V /(x) to state the necessary first- and second-order conditions for unconstrained optimality ... 

As indicated in Table 4.2, the eigenvalues of the Hessian matrix of fix) indicate the shape of a function. For a positive-definite symmetric matrix, the eigenvectors (refer to Appendix A) form an orthonormal set. For example, in two dimensions, if the eigenvectors are Vj and v2, v[v2 =0 (the eigenvectors are perpendicular to each other). The eigenvectors also correspond to the directions of the principal axes of the contours of fix). 

In optimization the matrix Q is the Hessian matrix of the objective function, H. For a quadratic function /(x) of n variables, in which H is a constant matrix, you are guaranteed to reach the minimum of/(x) in n stages if you minimize exactly on each stage (Dennis and Schnabel, 1996). In n dimensions, many different sets of conjugate directions exist for a given matrix Q. In two dimensions, however, if you choose an initial direction s1 and Q, s2 is fully specified as illustrated in Example 6.1. 

Steepest descent can terminate at any type of stationary point, that is, at any point where the elements of the gradient of /(x) are zero. Thus you must ascertain if the presumed minimum is indeed a local minimum (i.e., a solution) or a saddle point. If it is a saddle point, it is necessary to employ a nongradient method to move away from the point, after which the minimization may continue as before. The stationary point may be tested by examining the Hessian matrix of the objective function as described in Chapter 4. If the Hessian matrix is not positive-definite, the stationary point is a saddle point. Perturbation from the stationary point followed by optimization should lead to a local minimum x. ... 

Find a direction conjugate to s with respect to the Hessian matrix of the objective function /(x) = xx + 2x — xxx2 at the same point. 

Is it necessary that the Hessian matrix of the objective function always be positive-definite in an unconstrained minimization problem ... 

Show how to make the Hessian matrix of the following objective function positive-definite at x = [1 l]r by using Marquardt s method ... 

If no active constraints occur (so x is an unconstrained stationary point), then (8.32a) must hold for all vectors y, and the multipliers A and u are zero, so V L = V /. Hence (8.32a) and (8.32b) reduce to the condition discussed in Section 4.5 that if the Hessian matrix of the objective function, evaluated at x, is positive-definite and x is a stationary point, then x is a local unconstrained minimum of/. 

The second order sufficiency conditions show that the first two of these three Kuhn-Tucker points are local minima, and the third is not. The Hessian matrix of the Lagrangian function is... 

The condition number of the Hessian matrix of the objective function is an important measure of difficulty in unconstrained optimization. By definition, the smallest a condition number can be is 1.0. A condition number of 105 is moderately large, 109 is large, and 1014 is extremely large. Recall that, if Newton s method is used to minimize a function/, the Newton search direction s is found by solving the linear equations... 

Indefinite quadratic programs, in which the constraints are linear and the objective function is a quadratic function that is neither convex nor concave because its Hessian matrix is indefinite. 

For a scalar function, the matrix of second derivatives, called the Hessian matrix, is... 

This function is plotted on Figure 3.9 and shows a regular pattern of maxima and minima. Its Hessian matrix is... 

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