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**Function Optimization Newton’s Method. Conjugate Gradients**

Lecture 6 Function Optimization Newton’s Method. Conjugate Gradients Math for CS Lecture 6

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Newton’s Method In Lecture 5 we have seen that the steepest descent method can suffer from slow convergence. Newton’s method fixes this problem for cases, where the function f(x) near x* can be approximated by a paraboloid: ,where and (1) Math for CS Lecture 6

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Newton’s Method 2 Here gk is the gradient and Qk is the Hessian of the function f, evaluated at xk . They appear in the 2nd and 3rd terms of the Taylor expansion of f(xk). Minimum of the function should require: The solution of this equation gives the step direction and the step size towards the minimum of (2), which is, presumably, close to the minimum of f(x). The minimization algorithm in which xk+1=y(xk)=xk+∆, with ∆ defined by (2) is called a Newton’s method. (2) Math for CS Lecture 6

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Newton’s Method 3 The greater speed of Newton's method over steepest descent is borne out by analysis: while steepest descent has a linear order of convergence, Newton's method is quadratic. In fact, let be the place reached by a Newton step starting at x. Suppose that at the minimum x* the Hessian Q(x*) is nonsingular. Then Since g(x*)=0 Math for CS Lecture 6

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**Newton’s Method 4 And We can estimate the difference |xk+1-x*| :**

where is some point on the line between x* and xk. Math for CS Lecture 6

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**Newton’s Method is Quadratic**

Since y(x*) = x*, the first derivatives of y at x* are zero, so that the first term in the right-hand side above vanishes, and Thus, the convergence rate of Newton's method is of order at least two. Math for CS Lecture 6

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**Newton’s Method is Quadratic 2**

For example, for a quadratic function The steepest descent takes many iterations to converge, while the Newton’s method will require only one step. However, this single iteration in Newton's method is more expensive, because it requires both the gradient gk and the Hessian Qk to be evaluated, for a total of derivatives . In addition, the Hessian must be inverted, or, at least, a system (2) must be solved. (3) Math for CS Lecture 6

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**Newton’s Method vs Steepest Descent**

In contrast, steepest descent requires the gradient gk for selecting the step direction Pk, and a line search in the direction pk to find the step size. These faster step can be advantageous over faster convergence of Newton’s method for large dimensionality of x, which can exceed many thousands. The method of conjugate gradients, discussed in the following slides is motivated by the desire to accelerate convergence with respect to the steepest descent method, but without paying the storage cost of Newton's method. Math for CS Lecture 6

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Conjugate Gradient Suppose that we want to minimize the quadratic function where Q is a symmetric, positive definite matrix, and x has n components. As we saw in explanation of steepest descent, the minimum x* is the solution to the linear system The explicit solution of this system requires about O(n3) operations and O(n2) memory, what is very expensive. Math for CS Lecture 6

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Conjugate Gradients 2 We now consider an alternative solution method that does not need Q, but only the gradient of f(xk) evaluated at n different points x1 , . . ., xn. Gradient Conjugate Gradient Math for CS Lecture 6

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Conjugate Gradients 3 Consider the case n = 3, in which the variable x in f(x) is a three-dimensional vector . Then the quadratic function f(x) is constant over ellipsoids, called isosurfaces, centered at the minimum x* . How can we start from a point xo on one of these ellipsoids and reach x* by a finite sequence of one-dimensional searches? In the steepest descent, for the poorly conditioned Hessians orthogonal directions lead to many small steps, that is, to slow convergence. Math for CS Lecture 6

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**Conjugate Gradients: Spherical Case**

When the ellipsoids are spheres, on the other hand, the convergence is much faster: first step takes from x0 to x1 , and the line between x0 and x1 is tangent to an isosurface at x1 . The next step is in the direction of the gradient, takes us to x* right away. Suppose however that we cannot afford to compute this special direction p1 orthogonal to po, but that we can only compute some direction p1 orthogonal to po (there is an n-1 -dimensional space of such directions!) and reach the minimum of f(x) in this direction. In that case n steps will take us to x* of the sphere, since coordinate of the minimum in each on the n directions is independent of others. Math for CS Lecture 6

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**Conjugate Gradients: Elliptical Case**

Any set of orthogonal directions, with a line search in each direction, will lead to the minimum for spherical isosurfaces. Given an arbitrary set of ellipsoidal isosurfaces, there is a one-to-one mapping with a spherical system: if Q = UEUT is the SVD of the symmetric, positive definite matrix Q, then we can write ,where (4) (5) Math for CS Lecture 6

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Elliptical Case 2 Consequently, there must be a condition for the original problem (in terms of Q) that is equivalent to orthogonality for the spherical problem. If two directions qi and qj are orthogonal in the spherical context, that is, if what does this translate into in terms of the directions pi and pj for the ellipsoidal problem? We have (6) Math for CS Lecture 6

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**Elliptical Case 3 Consequently, What is**

This condition is called Q-conjugacy, or Q-orthogonality : if equation (7) holds, then pi and pj are said to be Q-conjugate or Q-orthogonal to each other. Or simply say "conjugate". (7) Math for CS Lecture 6

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Elliptical Case 4 In summary, if we can find n directions p0, . . .,pn_1 that are mutually conjugate, i.e. comply with (7), and if we do line minimization along each direction pk, we reach the minimum in at most n steps. Of course, we cannot use the transformation (5) in the algorithm, because E and especially UT are too large. So we need to find a method for generating n conjugate directions without using either Q or its SVD . Math for CS Lecture 6

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**Hestenes Stiefel Procedure**

Where Math for CS Lecture 6

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**Hestenes Stiefel Procedure 2**

It is simple to see that pk and pk+1 are conjugate. In fact, The proof that pi and pk+1 for i = 0, , k are also conjugate can be done by induction, based on the observation that the vectors pk are found by a generalization of Gram-Schmidt to produce conjugate rather than orthogonal vectors. Math for CS Lecture 6

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Removing the Hessian In the described algorithm the expression for yk contains the Hessian Q, which is too large. We now show that yk can be rewritten in terms of the gradient values gk and gk+1 only. To this end, we notice That Or Proof: So that Math for CS Lecture 6

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**Removing the Hessian 2 We can therefore write and Q has disappeared .**

This expression for yk can be further simplified by noticing that because the line along pk is tangent to an isosurface at xk+l , while the gradient gk+l is orthogonal to the isosurface at xk +l. Math for CS Lecture 6

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**Polak-Ribiere formula**

Similarly, Then, the denominator of yk becomes In conclusion, we obtain the Polak-Ribiere formula Math for CS Lecture 6

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General Case When the function f(x) is arbitrary, the same algorithm can be used, but n iterations will not suffice, since the Hessian, which was constant for the quadratic case, now is a function of xk. Strictly speaking, we then lose conjugacy, since pk and pk+l are associated to different Hessians. However, as the algorithm approaches the minimum x*, the quadratic approximation becomes more and more valid, and a few cycles of n iterations each will achieve convergence. Math for CS Lecture 6

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