Most of the limitations of NRM in multiple dimensions are the same as those for NM in one dimension. The main issues stem from three major areas (per usual), initial point, derivatives, and function evaluations.

Our initial value for Newton’s method greatly determines the effectiveness.The closer our initial guess is to our minima the better the method will work. However, this requires some intuition of the function which can get quite complex. Lets take a look at two different initial points (20,20) and (2,2). The graph of (20,20) method is below.

Screen Shot 2015-07-18 at 11.16.51 AM

As you can see the method is flying back and forth past our minima. However, the method eventually converges at (0.9987, 0.4987), our target minima. Below I have included a matrix containing our initial step and all of the following steps (taken from matlab output).

Screen Shot 2015-07-18 at 11.26.46 AM

Now lets try it again with our other initial point (2,2). This is already close to our minimum so it shouldn’t take too many iterations.

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The closer initial staring point converged in a fifth of the iterations which means a less function evaluations. This brings us to our next two issues, derivatives and function evaluations. We need to be able to find the Hessian of our function as well as the gradient. If we cannot take these two we will need to pick a different method or modify our existing. Most problems stem from the Hessian. Sometime the Hessian cannot be found, other times it can be found but not inverted, but most of the time it is just too expensive to evaluate or invert. Mathematicians have come up with alternative methods called quasi-newton methods (these actually stem from the secant methods I mentioned before) which seek to remedy this issue. Like the one dimensional version, there are a lot of function evaluations.  Each derivative of the gradient must be evaluated as well as all of the second derivatives that make up the hessians. The hessian then needs to be inverted which is expensive at higher dimensions.

Now you have two tools to deal with finding minima at higher dimensions. Up next we will explore new more advanced methods for optimization.


Gradient Search Methods: Newton-Raphson Method Part 2

Last post we got an idea of how Newton-Raphson works (and how similar it is to the one dimensional version). Here we will explore step by step how the algorithm works and how quickly it arrives to our minimum.  We will be using the same function as before so we can compare how quickly the method descends. Here is our function in both a surface plot and a contour plot as a reminder. (f(x,y) =  x^2 -x+cos(y+x)+y^2

Screen Shot 2015-06-30 at 9.39.18 PMScreen Shot 2015-06-30 at 9.47.29 PM

As you can see it is a relatively simple surface and convex. In order to use Newton Raphson, we need to take both the gradient and the hessian of our function. A quick mathematics reminder, The gradient is the vector which contains first derivatives of the function with respect to each of its variables. Below you can see a general case for the gradient as well our specific gradient for this function.

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As I mentioned last post we also need the equivalent of the second derivative for newton-raphson. This takes the form of the hessian. Another quick mathematics reminder, the Hessian is the square matrix consisting of all the second derivatives of our function. The Hessian exhaustively takes the derivatives of the function with respect to one variable an d the second derivative with respect to every variable again. To clarify this i have left the general case below along with our specific case.

Screen Shot 2015-07-18 at 8.14.09 AM

Note that the Hessian above is a square matrix (it always should be). The second check that has to be made is to ensure that the Hessian is invertible. Since we are dealing with matrix we cannot “divide” matrices, we must use the inverse of the Hessian instead. This comes into play with our iterative step. Now to start our method we need an initial point, we are going to use the same one as we did with Steepest Descent (8,8).  With these values we calculate our Hessian and Gradient which gives us the following. Screen Shot 2015-07-18 at 8.58.19 AMWe now take the inverse of the Hessian and then find our next point. The iterative equation is listed below as well as the output.

Screen Shot 2015-07-18 at 9.10.17 AM

Our new point was a considerable decrease from our starting point. We repeat the above steps until we reach our stopping conditions which I set as our normal tolerance limits. I allowed the method to iterate until these conditions were met. The steps are outlined in the plot below.

Screen Shot 2015-07-18 at 9.43.38 AM

The graph above looks similar to our dynamic steepest descent. As you can see the method was very direct. Taking only 5 iterations our method produces a minimum of (0.9987,0.4987). This is pretty much dead on to the actual minimum of our function. Newton Raphson makes quick work of the minimization. Next post we will look at the limitations of this method. Code below.


%newton raphson in  dimensions
%f(x,y) =  x^2 -x+cos(y+x)+y^2


x(1,1)=8; %initial guess



while tol(1) >0.0001 && tol(2) > 0.0001 %stop conditions

    dx1 = -1+2*x(1,i-1)-sin(x(1,i-1)+x(2,i-1)); %gradient dx
    dx2 = 2*x(2,i-1) - sin(x(1,i-1)+x(2,i-1)); %gradient dy

    g=[dx1,dx2]';%gradient matrix
    ddx1= 2 - cos(x(1,i-1)+x(2,i-1)); % sames as ddx2
    dx1dx2= -cos(x(1,i-1)+x(2,i-1)); % same as dx2dx1

    h =[ddx1 dx1dx2;dx1dx2,ddx1]; %hessian matrix
    x(:,i) = x(:,i-1)-hg;
    tol(1) = abs(x(1,i)-x(1,i-1)); %tolerance calc
    tol(2) = abs(x(2,i)-x(2,i-1));

    z(i) =x(1,i)^2 -x(1,i)+cos(x(2,i)+x(1,i))+x(2,i)^2; % function eval


Gradient Search Methods: Newton-Raphson Method Part 1

This method is gonna feel more than a little familiar. But before we get to the nitty gritty I have to give a disclaimer. This method is not purely a gradient method, as it does not solely rely on the gradient as a method of optimization. The method uses the gradient along with other properties of the function to arrive at our minimum. Now that that has been cleared up let move onto the method.

We are going to take a similar approach as we did with our one dimensional search methods. Steepest Descent reminds us of the golden section search and the fibonacci search. While they are used differently there are prominent similarities. When we looked at Steepest descent we had two cases, one with a constant step size and another with a variable step size. Eventually we reached an optimal case for these methods and we needed a new method. At this point we moved on to Newton’s method. Well, we are going to do that again, only in higher dimensions.

The next part is gonna be verrrrry familiar.

As you know the first and most important thing when thinking about using Newton-Raphson method is to ensure that the function is twice differentiable.  Like in one dimension, Newton-Raphson method uses a truncated taylor series to estimate where the minimum lies.

Newton-Raphson method uses a single point to begin. This point is used as a basis for our Taylor expansion. We need to take a brief detour to go over why we want to construct a Taylor Expansion. By doing this we create a quadratic approximation of our function locally, which we can in turn minimize. We repeat this until we hit a stationary point or our minimum.  So lets start with the Taylor series in N-dimensions, however we are only going to focus on the first three terms (this gives us up to the quadratic approximation).

Screen Shot 2015-07-16 at 9.19.58 PM

The above equation looks a little different than our previous Taylor expansion. As you can see there are no squares and no derivatives or  second derivatives (well not completely true).The first derivative is here is none other than our Gradient which is denoted by g. However, if you remember from the last time we dealt with the taylor series the third term deals with the second derivative. In n dimensions we need  an equivalent to the second derivative, for this we use the Hessian Matrix, which I have denoted as F. Now we use our first order necessary condition for optimality, this sets the gradient of our function equal to zero. From this we can determine our iterative step.

Screen Shot 2015-07-16 at 9.56.46 PM

This looks awfully like our iterative steps from our one dimensional Newton’s method. .  As we get closer to our minimum the gradient, g, of our function moves toward 0 which makes xk≈ xk+1. This is when we know our method has found a minimum (hopefully). Hopefully this all feels familiar. Next post we will see how Newton Raphson works.


Gradient Search Methods: Steepest descent Part 3

We have looked at the method behind steepest descent and explored two ways of calculating our step size. In this post we will look at the limitations and the best use practices for the method of steepest descent. 

Overall the method of steepest descent is a reliable and standard test method. However, the sacrifice is speed. When the functions get more complicated and the evaluations more expensive the method of steepest descent begins to lag behind other methods.  This tradeoff is an important one, as the desire to reach a minimum is counteracted by the waiting time.

The method is steepest descent is generally used when a minimum must be found. This search method would not be appropriate for functions which need to be optimized quickly and roughly. This also brings us to our step finding method. 

Between the step finding methods we can drastically change the behavior of our search method. It also may require us to take derivatives of our function (which may or may not be applicable at our test points). A standard step size might be useful to minimize derivative evaluations, but at the cost of time. Nevertheless, the method of steepest is a reliable and consistent method. 

It is necessary to note that for any applicable function, the method of steepest descent will find a minimum. This guarentee must be noted when evaluating proper search methods, and makes steepest descent necessary for anyone’s optimization quiver. 

Gradient Search Methods: Steepest Descent Part 2

If you followed from last post we derived (loosely) our iterative step. With that we can go about with out steepest descent method. We start by choosing an initial guess for our function, for this function i have chosen (8,8). This is plotted below on the level curve.

Screen Shot 2015-07-07 at 9.16.48 PM

From this point we must calculate the gradient. I have taken the gradient of our function (f(x,y) =  x^2 -x+cos(y+x)+y^2). This gradient must now be evaluated at our point (8,8)

Screen Shot 2015-07-07 at 9.21.46 PM

As I mentioned earlier we really need two things for our method, a direction and a step size. With the gradient evaluated we have our direction, now we need our step size. This is where we have options. For the step size we have 2 general options. We can choose a constant step size (think GSS) or we can have a dynamic step size (think FS and NM). In this post I will show both.

For constant step size we have a few issues. As you can see in our level plots we are not exactly close to our minimum. so we can choose a large step size to get close fast. However, once we get close to our minimum the large step size will cause us to over shoot. Trying to correct the over shoot the method will then over shoot in the opposite direction. So that leaves us with choosing a smaller step size. Well this brings its own problems. The small step size takes a much longer time to reach our minimum when our initial guess is far. I went with the smaller step size for the constant step size version.

What about the dynamic step size? What we are actually doing with the dynamic step size is solving an optimization problem. We are finding the step size that minimizes our function the most. We want to minimize the the following f(x-alpha*d) where d is the gradient evaluated at x. Here are a few options to minimize this. We can use an ODSM from before to calculate a estimate of the best step size or we can use them to find the exact best step size. Both of these become very expensive with little benefit. Instead we use a method called backtracking line search. This method gives us an acceptable step size while not being to computationally heavy. With backtracking line search, we start with an initial step size,alpha, and we check if x+alpha*d is an acceptable point. WE do this by using the Armijo-Goldstein Condition. This condition ensures that the step size we take makes our function decrease. If it does not satisfy this condition we half our alpha and try again. We try until the condition is satisfied. When it is satisfied we continue with out descent method.

Now that we have our two methods, lets check how they compare. Here are the first three steps. On the left is our constant step size, on the right is backtracking line search step.

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As you can see the constant step size is slowly making its way there, however, the dynamic step size has already over shot the minimum and is backtracking. Lets try another three steps.

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More of the same in these graphs. The constant step size is slowly chugging along, while the dynamic step size has arrived at the minimum and waiting for the other method to catch up. The dynamic step size plot looks almost identical to the one before. This is because the method is overshooting and backtracking over the minimum until the steps get smaller than our tolerance. After that point our method stops and gives us our minimum. If we let both methods terminate we find that the constant step size takes over 40 iterations while the dynamic only takes 6. Now what about accuracy. Our minimum for our function is around (0.99865, 0.49865). Our constant step size gives us a minimum of (0.9990,0.4991), dynamic gives us (0.9986,0.4986). It looks like we have a clear winner. In this case, the dynamically changing step size not only beats the constant in number of iterations, but in accuracy as well.

I hope this clarified steepest descent. Next post we go over the limitations to this method (it will be brief there aren’t a ton). I have added my code below which is separated into constant and dynamic. Enjoy

% steepest decent 
%f(x,y) =  x^2 -x+cos(y+x)+y^2.

%% constant step size
x = zeros(1,10);% initialize arrays
y = zeros(1,10);

x(1)=8; %initial guess

%dx = -1+2*x-sin(x+y)
%dy = 2*y-sin(x+y)

a0=0.1; %initial step size



while tol(1) >0.0001 && tol(2) > 0.0001 %stop conditions

    dx = -1+2*x(i)-sin(x(i)+y(i)); %gradient dx
    dy = 2*y(i) - sin(x(i)+y(i)); %gradient dy
    x(i) = x(i-1)-dx*a0; %iterative steps
    y(i) = y(i-1)-dy*a0;
    tol(1) = abs(x(i)-x(i-1)); %tolerance calc
    tol(2) = abs(y(i)-y(i-1));

%% Backtracking line search

x = zeros(1,10);
y = zeros(1,10);


%dx = -1+2*x-sin(x+y)
%dy = 2*y-sin(x+y)




while tol(1) >0.0001 | tol(2) > 0.0001

    dx = -1+2*x(i)-sin(x(i)+y(i));
    dy = 2*y(i) - sin(x(i)+y(i));

    %evaluate function at different points
    fc =  x(i)^2 -x(i)+cos(y(i)+x(i))+y(i)^2;
    newfx =(x(i)-dx*ax)^2 -(x(i)-dx*ax)+cos(y(i)+(x(i)-dx*ax))+y(i)^2;
    newfy =x(i)^2 -x(i)+cos((y(i)-dy*ay)+x(i))+(y(i)-dy*ay)^2;
    while newfx> fc -ax*dx %Armijo-Goldstein Condition check
        ax=.5*ax; %shrink step size
        % function eval at new step
        newfx =(x(i)-dx*ax)^2 -(x(i)-dx*ax)+cos(y(i)+(x(i)-dx*ax))+y(i)^2;
    while newfy > fc -ay*dy
        newfy =x(i)^2 -x(i)+cos((y(i)-dy*ay)+x(i))+(y(i)-dy*ay)^2;
    x(i) = x(i-1)-dx*ax;
    y(i) = y(i-1)-dy*ay;
    tol(1) = abs(x(i)-x(i-1));
    tol(2) = abs(y(i)-y(i-1));

Gradient Search Methods: Steepest Descent Part 1

If you have been following along we have worked through a bunch of one dimensional search methods. However, what happens when we need to perform a search on multiple dinensions?  Well we need a new search method that can be expanded to multiple variables while still finding a minimum (or maximum).

The method should work similar to our previous methods. However, it should be applicable up to n variable. With this in mind we set out to design a search method. As we have seen before, we need two things for a successful search method. First we need to know where we are going (direction) and second we need to know how far to go in that direction (step size). With these two pieces of information we can go about finding our minimum (or maximum).

Let’s focus on the direction first. We want our function to decrease fastest on our chosen direction. If we look at a multi variable function which is both defined an differentiable in a small area around a point (otherwise known as a neighborhood), we find that the function decreases fastest along the negative gradient of the function. Let’s test this out.

Let’s take a simple multivariable function f(x,y) =  x^2 -x+cos(y+x)+y^2.  Plotting this produces the following 3D graph and contour plot. Take notice of the Contour plot it will help us to determine our direction.

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We have an infinite number of directions to move In but let’s choose four test cases. We will choose the gradient, the negative gradient, and two other directions. For our initial point lets choose (-6,8). This point lets use evaluate our gradient Mapping these directions (all with a step of 0.1 ) produces the following graph.

Screen Shot 2015-06-30 at 10.26.20 PM

Now this isn’t a rigorous proof (or a particularly fantastic one) but as you can see our function is lowest in the direction of the negative gradient. As you can see the negative gradient line (magenta) points toward the center of the contour plot and to the lowest function evaluation. All other points are off center or in completely wrong directions. However, this only holds for a small neighborhood around our point. If we take too big a step then we might end up a wrong point or higher function evaluation. This brings us to our next requirement a step size.  There are a few ways to determine a step size. The two major ways are a static step size (kinda like how we reduced our interval in GSS), and a dynamically changing step size. Regardless of which we end up choosing lets represent the step size by rho, ρ.

We now have our step size (ρ) and our direction (negative gradient). With this we can begin our search. With initial point “a” we evaluate the gradient. The gradient is then scaled by our step size and subtracted to from our initial point. This gives us our second point “b.” We then repeat this with point “b.” Evaluate the gradient, scale by the step size, subtract from point “b” to give us point “c”. This is outlined below.

Screen Shot 2015-06-30 at 7.22.50 PM

So you probably realized where we were headed. Following the steps above we have determined our iterative step. Like our previous one-dimensional search methods, our gradient search method iterates the above equation until a tolerance condition is met. The method described above is called the Method of Steepest Descent or Gradient Descent. Next Post we will go over the method with a real example.