Saturday, December 15, 2012

Golden Section Search

Golden Ratio seen in nature
  Last time, I demonstrated how to implement a gradient descent optimization algorithm, which is widely used in artificial intelligence to optimize neural networks.  It is versatile, able to accept any multidimensional input function, and simple to understand and implement.  However, you wouldn't be likely to find this in a situation where performance matters... it takes many iterations to find a local minimum.  If it is expensive to execute your function being minimized, you rapidly discover the importance of performance in optimization, and hence the large number of approaches.  Another drawback is that Gradient Descent doesn't provide a simple mechanism for constraining the search.  If you know the region where a minimum must exist, you need to look for another approach.

  Enter the Golden Section Search, a one dimensional search tool with a number of benefits over gradient descent.  First, it is a class of minimization techniques which work by reducing a bracket containing a minimum.  This means we can preset the bracket based on prior information, which can be a major upside.  Second, it is typically more efficient than a gradient descent search.  The downside is that it is limited to a single input dimension, although it can be combined with a conjugate gradient search method, minimizing a single value in the current search direction.

Golden Section Search Diagram

  So how does this approach work?  Lets start with the above graph, where we have some unknown function f(x), and three points (x1, f1), (x2, f2), (x3, f3) which we already know something about.  x4 is our next candidate x value, and f4a and f4b are possible y values for this candidate.  We remember from the extreme value theorem that there will be a local minimum within a bracket when there is a point inside the bracket that falls below the line connecting the bracketing points.  In this case, f2 clearly falls below the line connecting f1 and f3.  Now we pick our bracketing points in a way that there will always be a slightly bigger side, and our next x value will be in the side with the bigger boundary.  In this case, b is bigger than a (along the x-axis in the diagram), so our next point will be to the right of x2.  Now with your minds eye, you can think of a parabola passing through f1, f2, and f4a, and you can see that the minimum value is in between f4a and f1... where as there would be a maximum if you made a parabola between f2, f4a, and f3.  Because of this, you would use x1, x2, and x4 as your next three bracket points.  If instead we were to do the same thing with f4b, the bracket would instead work out to x2, x4, and x3.  This process is recursively repeated until your bracket is small enough to achieve a desired precision.

Now some code.

    public delegate double Function1D(double x);

    public class GoldenSectionSearch
    {
        private readonly double _goldenRatio = 2.0 - (1.0 + Math.Sqrt(5.0))/2.0;
        public double Threshold { get; set; }

        public GoldenSectionSearch()
        {
            Threshold = 0.001;
        }

        public double Minimize(Function1D function, double lowerBound, double upperBound)
        {
            var f = CachedFunction(function);
            var midpoint = StartingSearchPoint(lowerBound, upperBound);
            return Minimize(f, lowerBound, midpoint, upperBound);
        }

        private double Minimize(Function1D f, double lower, double midpoint, double upper)
        {
            var x = NextSearchPoint(lower, midpoint, upper);
            if (IsSearchDone(lower, midpoint, upper, x))
                return FinalPosition(lower, upper);

            if (IsMinimumInNewSearchBracket(f, midpoint, x))
            {
                if (IsUpperBracketLarger(lower, midpoint, upper))
                    return Minimize(f, midpoint, x, upper);
                return Minimize(f, lower, x, upper);
            }
            else
            {
                if (IsUpperBracketLarger(lower, midpoint, upper))
                    return Minimize(f, lower, midpoint, x);
                return Minimize(f, x, midpoint, upper);
            }
        }

        private Function1D CachedFunction(Function1D function)
        {
            var cache = new Dictionary();
            Function1D f = (x) =>
            {
                if (cache.ContainsKey(x))
                {
                    return cache[x];
                }
                var result = function(x);
                cache[x] = result;
                return result;
            };
            return f;
        }

        private double StartingSearchPoint(double lower, double upper)
        {
            return (upper - lower)*_goldenRatio + lower;
        }

        private double NextSearchPoint(double lower, double midpoint, double upper)
        {
            if(IsUpperBracketLarger(lower, midpoint, upper))
                return midpoint + _goldenRatio * (upper - midpoint);
            return midpoint - _goldenRatio * (midpoint - lower);
        }

        private bool IsSearchDone(double lower, double midpoint, double upper, double x)
        {
            return Math.Abs(upper - lower) < Threshold*(Math.Abs(midpoint) + Math.Abs(x));
        }

        private double FinalPosition(double lower, double upper)
        {
            return (upper + lower)/2.0;
        }

        private bool IsUpperBracketLarger(double lower, double midpoint, double upper)
        {
            return (upper - midpoint > midpoint - lower);
        }

        private bool IsMinimumInNewSearchBracket(Function1D f, double midpoint, double x)
        {
            return f(x) < f(midpoint);
        }
    }

Some explanation here...

First we use a function delegate to force the user to provide a 1d function to minimize.  It is less flexible than the gradient descent delegates, but a necessary feature of this algorithm.

Inside the public Minimize function, we wrap the input with a caching decorator, so that any value we have already evaluated is reused.

The search termination criteria in the IsSearchDone is somewhat confusing, but a recommended formula by the numerical recipes guys.  The gist is we say a minimum is found when the width of the bracket is smaller than a precision that we care about.

We use the "golden ratio" to pick the size of our bracket steps.  This gives us special properties when we are reducing the size of the bracket to make sure the ratios never change, and also minimizes the number of steps required to reach the minimum.  It is a surprising result that it is actually more efficient than bisection.

The private Minimize helper function is the workhorse, where the recursive search happens.  Each iteration, we check to see if we are done, and if not, reduce the bracket, and pick a lower, midpoint, and upper bounds for the next iteration based on the intuition described above the code.

So now a test to show it in action....

        [Test]
        public void GoldenSectionSearchWorks()
        {
            var gss = new GoldenSectionSearch { Threshold = 0.0001 };
            const double lowerBounds = -10.0;
            const double upperBounds = 10.0;
            Function1D f = (x) => Math.Pow(x - 4.0, 2.0);
            var solution = gss.Minimize(f, lowerBounds, upperBounds);
            Assert.AreEqual(solution, 4.0, 0.001);
        }

And we are done!  A C# Golden Section Search algorithm.

Saturday, December 1, 2012

Gradient Descent



Whats the right plan to achieve our goals?
  I've come across this story many times in my career... tell me if it sounds familiar to you:  I'm working on a program that has to make a decision about what to do next.  I can evaluate the outcome for whatever decision I make, but I can't predetermine which actions will lead to the best solution.  This is a situation that calls for numerical optimization techniques.  The use of numerical optimization is pervasive throughout computer science.  Artificial intelligence uses it to optimize neural networks.  Engineering tools use it to improve the design of components.  Computer games use it to have the bad guys make decisions that look intelligent.  So how does it work?

Black Hole Gravity Field
  We can start by making a conceptual leap, illustrated by an example.  Suppose you are in a space ship, which you are trying to pilot to the center of a black hole.  You can't see where the black hole is, but you can feel its gravity.  You know that its gravity will get stronger as you get close to the center.  In this example, we can think of the effect of gravity on space as our optimization function.  If we graph that against our position, we get a plot with a nice hole where our black hole sits.  If we had a ball, all we would have to do is set it down on that graph, and let it slide downhill until it reached the center.

Gradient Descent


  This simple strategy is actually the formulation for the gradient descent algorithm.  If at each point, if we know the slope of our function to be optimized, we can just take small steps in the downhill direction.  Eventually, we will reach a point at the bottom of the hill, and that is where we stop.

Gradient Descent Formula
  With this understanding of what we are doing, it is actually very easy to understand the formulation.  Here, F(x) is our function being evaluated, x is our position in the optimization space, gamma is the size of the step willing to take, and n is the step number.  our position at step n+1 can be evaluated as our position at step n plus a small movement in the downhill direction.  When our update fails to sufficiently improve our evaluation, we assume that a local minimum has been reached, and the search is terminated.

Code Design

  From a usability perspective, we want a design flexible for reuse with any arbitrary function to be minimized.  We don't want the user to have to provide a second function describing the gradient of their optimization function.  And we want to allow the user to provide a good guess as a starting point for the optimization.  

        public double Precision { get; set; }
        public double Eps { get; set; }
        public int MaxIterations { get; set; }
        public GradientDescent()
        {
            Precision = 0.001;
            Eps = 0.1;
            MaxIterations = 1000;
        }

  We want to expose a couple parameters that effect the time it takes to reach a solution.  First, we expose the precision we are looking to achieve, typically a very small floating point number.  Next, we expose Eps, which is the step size we take each iteration.  This can be tuned to the scale of your inputs, but typically we still want fairly small steps.  Finally, we expose the maximum number of iterations.  This is a practical measure, as small errors in the gradient can lead to infinite loops near the minimum, while the optimizer bounces back and forth between two positions.  If we get stuck, we rely on the iterations to time out, and a satisfactory answer is returned.

public delegate double Function(List x);
public delegate List Derivative(List x); 

  To represent our optimization functions, the user must implement a function conforming to the first "Function" delegate pattern.  It is designed to map an n-dimensional input vector to a single output metric.  This makes it flexible enough to handle a wide variety of input functions.  We are going to use a second delegate to represent the local derivative, so that we can generate a functor wrapping the input function, generically providing the derivative for easy use.

        public double Distance(List a, List b)
        {
            return Math.Sqrt(a.Zip(b, (i, j) => Math.Pow(i - j, 2.0)).Sum());
        }

  We can recognize an ending connection when the next step is approximately the same location as the current step.  Note that at a local minimum, the gradient of F(x) goes to zero, and thus so does our step length.  We can use the Euclidean distance to measure how much our step has changed, and stop when this metric is sufficiently small.

        public Derivative Gradient(Function f)
        {
            return x => Enumerable.Range(0, x.Count)
                            .Select(i => (f(x.Select((y, j) => j == i ? y + Precision : y).ToList()) -
                                          f(x.Select((y, j) => j == i ? y - Precision : y).ToList()))
                                         / (2*Precision))
                            .ToList();
        }

If you reach back to your calculus class, the definition of the derivative gives us these simple formulations.  I choose to use central differencing, because it is slightly more accurate, but still only requires two function evaluations.

        public List Optimize(Function f, List seed)
        {
            var fPrime = Gradient(f);
            var cnt = 0;
            var x_old = seed.Select(x => x + Eps).ToList();
            var x_new = seed;
            while (Distance(x_new, x_old) > Precision && cnt < MaxIterations)
            {
                x_old = x_new;
                x_new = fPrime(x_old).Zip(x_old, (i, j) => j - Eps*i).ToList();
                cnt++;
            }
            return x_new;
        }

Finally, we have our actual optimization function.  the "seed" variable represents the starting point of the search, and good guesses can dramatically impact the performance of the algorithm.  Also, since we generically defined the Function delegate to take an arbitrary sized vector as input, we need to use the seed to infer a specific size to the inputs of our function.

        [Test]
        public void OptimizerWorks()
        {
            var gd = new GradientDescent();
            var seed = new List {0.0, 0.0};
            var solution = gd.Optimize((data) => { return Math.Pow(data[0] - 2, 2.0) + Math.Pow(data[1] - 4, 2.0); },
                                       seed);

            Assert.AreEqual(solution[0], 2.0, 0.001, "best x found");
            Assert.AreEqual(solution[1], 4.0, 0.001, "best y found");
        }

Now, we have a test showing how to use the module, and voila, a simple C# Gradient Descent module!