## Overview

The least squares package fits a non-linear parametric model to a set of observed values by minimizing a cost function with a specific form. The fitting consists in finding the values for some parameters $$p$$ that minimize a cost function $$J = \sum_i w_i(o_i - f_i)^2$$ where $$w_i$$ are the weights, $$o_i$$ are the observed values and $$f_i = f_i( p )$$ are the values computed from the model. The various $$r_i = o_i - f_i$$ terms are the residuals which quantify the deviation between the set of observed values and theoretical values computed from measurement models depending on the free parameters.

Among other properties, the non-linear least squares is the Maximum Likelihood Estimator (MLE) if the observations have normally distributed errors and the weights are set to the reciprocal of their variance. That is $$o_i = N(f_i(p), \sigma)$$ and $$w_i = \frac{1}{\sigma^2}$$ where $$N(\mu, \sigma)$$ denotes a normal distribution with mean $$\mu$$ and standard deviation $$\sigma$$. For more on non-linear least squares, its theory, and its properties the reader is encouraged to consult a text book such as [1].

Two engines devoted to least-squares problems are available. The first one is based on the Gauss-Newton method. The second one is the Levenberg-Marquardt method.

## LeastSquaresBuilder and LeastSquaresFactory

In order to solve a least-squares fitting problem, the user must provide the following elements:

• an implementation of the measurement model $$f(p)$$ and its Jacobian, $$\frac{\partial f}{\partial p}$$. This is best done by implementing MultivariateJacobianFunction.
• the observed (or target) values: $$o$$.
• the start values for all parameters: $$s$$.
• optionally a validator for the parameters $$p$$. This is an implementation of ParameterValidator.
• optionally weights for sample point: $$w$$, this defaults to 1.0 if not provided.
• a maximum number of iterations.
• a maximum number of model evaluations, which may be different than iterations for Levenberg-Marquardt.
• a convergence criterion, which is an implementation of ConvergenceChecker<Evaluation>

The elements of the list above can be provided as an implementation of the LeastSquaresProblem interface. However, this may be cumbersome to do directly, so some helper classes are available. The first helper is a mutable builder: LeastSquaresBuilder. The second helper is an utility factory: LeastSquaresFactory.

The builder class is better suited when setting the various elements of the least squares problem is done progressively in different places in the user code. In this case, the user would create first an empty builder and configure it progressively by calling its methods (start, target, model, …). Once the configuration is complete, calling the build method would create the least squares problem.

The factory utility is better suited when the various elements of the least squares problem are all known at one place and the problem can be built in just one sweep, calling to one of the static LeastSquaresFactory.create method.

## Model Function

The model function is used by the least squares engine to evaluate the model $$f(p)$$. It is therefore a multivariate function (it depends on the various $$p_k$$) and it is vector-valued (it has several components $$f_i$$). There must be exactly one model function $$f_i$$ for each observed (or target) value $$o_i$$. In order for the problem to be well defined, the number of parameters must be less than the number of observations. Failing to ensure this may lead to the engine throwing an exception as the underlying linear algebra operations may encounter singular matrices. It is not unusual to have a large number of observations (several thousands) and only a dozen parameters. There are no limitations on these numbers, though.

As the least squares engine uses the Jacobians matrices of the model function, both its value and its derivatives with respect to the parameters, $$p$$, must be available. There are two ways to provide this:

• an implementation of the measurement model $$f(p)$$ and its Jacobian, $$\frac{\partial f}{\partial p}$$. This is best done by implementing MultivariateJacobianFunction.
• the observed (or target) values: $$o$$.
• the start values for all parameters: $$s$$.
• optionally a validator for the parameters $$p$$. This is an implementation of ParameterValidator.
• optionally weights for sample point: $$w$$, this defaults to 1.0 if not provided.
• a maximum number of iterations.
• a maximum number of model evaluations, which may be different than iterations for Levenberg-Marquardt.
• a convergence criterion, which is an implementation of ConvergenceChecker<Evaluation>

The first alternative is best suited for models which are not computationally intensive as it allows more modularized code with one method for each type of computation. The second alternative is best suited for models which are computationally intensive and evaluating both the values and derivatives in one sweep saves a lot of work.

The point parameter of the value methods in the MultivariateVectorFunction, MultivariateMatrixFunction, or MultivariateJacobianFunction interfaces will contain the parameter vector $$p$$. The values will be the model values $$f(p)$$ and the derivatives will be the derivatives of the model values with respect to the parameters $$\frac{\partial f(p)}{\partial p}$$.

There are no requirements on how to compute value and derivatives. The DerivativeStructure class may be useful to compute analytical derivatives without pencil and paper, but this class is not mandated by the API which only expects the derivatives as a Jacobian matrix containing primitive double entries.

One non-obvious feature provided by both the builder and the factory is lazy evaluation. This feature allows to defer calls to the model functions until they are really needed by the engine. This can save some calls for engines that evaluate the value and the Jacobians in different loops (this is the case for Levenberg-Marquardt). However, lazy evaluation is possible only if the model functions are themselves separated, i.e. it can be used only with the first alternative above. Setting up the lazyEvaluation flag to true in the builder or factory and setting up the model function as one MultivariateJacobianFunction instance at the same time will trigger an illegal state exception telling that the model function misses required functionality.

## Parameters Validation

In some cases, the model function requires parameters to lie within a specific domain. For example a parameter may be used in a square root and needs to be positive, or another parameter represents the sine of an angle and should be within -1 and +1, or several parameters may need to remain in the unit circle and the sum of their squares must be smaller than 1. The least square solvers available in Hipparchus currently don’t allow to set up constraints on the parameters. This is a known missing feature. There are two ways to circumvent this.

Both ways are achieved by setting up a ParameterValidator instance. The input of the value and jacobian model functions will always be the output of the parameter validator if one exists.

One way to constrain parameters is to use a continuous mapping between the parameters that the least squares solver will handle and the real parameters of the mathematical model. Using mapping functions like logit and sigmoid, one can map a finite range to the infinite real line. Using mapping functions based on log and exp, one can map a semi-infinite range to the infinite real line. It is possible to use such a mapping so that the engine will always see unbounded parameters, whereas on the other side of the mapping the mathematical model will always see parameters mapped correctly to the expected range. Care must be taken with derivatives as one must remember that the parameters have been mapped. Care must also be taken with convergence status. This may be tricky.

Another way to constrain parameters is to simply truncate the parameters back to the domain when one search point escapes from it and not care about derivatives. This works only if the solution is expected to be inside the domain and not at the boundary, as points out of the domain will only be temporary test points with a cost function higher than the real solution and will soon be dropped by the underlying engine. As a rule of thumb, these conditions are met only when the domain boundaries correspond to unrealistic values that will never be achieved (null distances, negative masses, …) but they will not be met when the domain boundaries are more operational limits (a maximum weight that can be handled by a device, a minimum temperature that can be sustained by an instrument, …).

## Tuning

Among the elements to be provided to the least squares problem builder or factory are some tuning parameters for the solver.

The maximum number of iterations refers to the engine algorithm main loop, whereas the maximum number of evaluations refers to the number of calls to evaluate the model. Some algorithms (like Levenberg-Marquardt) have two embedded loops, with iteration number being incremented at outer loop level, but a new evaluation being done at each inner loop. In this case, the number of evaluations will be greater than the number of iterations. Other algorithms (like Gauss-Newton) have only one level of loops. In this case, the number of evaluations will equal to the number of iterations. In any case, the maximum numbers are really only intended as safeguard to prevent infinite loops, so the exact value of the limit is not important so it is common to select some almost arbitrary number much larger than the expected number of evaluations and use it for both maxIterations and maxEvaluations. As an example, if the least squares solver usually finds a solution in 50 iterations, setting a maximum value to 1000 is probably safe and prevents infinite loops. If the least squares solver needs several hundreds of evaluations, it would probably be safer to set the maximum value to 10000 or even 1000000 to avoid failures in slightly more demanding cases. Very fine tuning of these maximum numbers is often worthless, they are only intended as safeguards. One can think of these parameters as the divergence criteria.

Convergence checking is delegated to a dedicated interface from the optim package: ConvergenceChecker, parameterized with either the specific Evaluation class used for least squares problems or the general PointVectorValuePair. Each time convergence is checked, both the previous and the current evaluations of the least squares problem are provided, so the checker can compare them and decide whether convergence has been reached or not. The predefined convergence checker implementations that can be useful for least squares fitting are:

• an implementation of the measurement model $$f(p)$$ and its Jacobian, $$\frac{\partial f}{\partial p}$$. This is best done by implementing MultivariateJacobianFunction.
• the observed (or target) values: $$o$$.
• the start values for all parameters: $$s$$.
• optionally a validator for the parameters $$p$$. This is an implementation of ParameterValidator.
• optionally weights for sample point: $$w$$, this defaults to 1.0 if not provided.
• a maximum number of iterations.
• a maximum number of model evaluations, which may be different than iterations for Levenberg-Marquardt.
• a convergence criterion, which is an implementation of ConvergenceChecker<Evaluation>

Of course, users can also provide their own implementation of the ConvergenceChecker interface.

## Optimization Engine

Once the least squares problem has been created, using either the builder or the factory, it is passed to an optimization engine for solving. Two engines devoted to least-squares problems are available. The first one is based on the Gauss-Newton method. The second one is the Levenberg-Marquardt method. For both increased readability and in order to leverage possible future changes in the configuration, it is recommended to use the fluent-style API to build and configure the optimizers. This means creating a first temporary version of the optimizer with a default parameterless constructor, and then to set up the various configuration parameters using the available withXxx methods that all return a new optimizer instance. Only the final fully configured instance is used. As an example, setting up a Levenberg-Marquardt with all configuration set to default except the cost relative tolerance and parameter relative tolerance would be done as follows:

  LeastSquaresOptimizer optimizer = new LevenbergMarquardtOptimizer().
withCostRelativeTolerance(1.0e-12).
withParameterRelativeTolerance(1.0e-12);


As another example, setting up a Gauss-Newton optimizer and forcing the decomposition to SVD (the default is QR decomposition) would be done as follows:

  LeastSquaresOptimizer optimizer = new GaussNewtonOptimizer().
withDecomposition(GaussNewtonOptimizer.Decomposition.SVD);


## Solving

Solving the least squares problem is done by calling the optimize method of the optimizer and passing the least squares problem as the single parameter:

  LeastSquaresOptimizer.Optimum optimum = optimizer.optimize(leastSquaresProblem);


The LeastSquaresOptimizer.Optimum class is a specialized Evaluation with additional methods te retrieve the number of evaluations and number of iterations performed. The most important methods are inherited from the Evaluation class and correspond to the point (i.e. the parameters), cost, Jacobian, RMS, covariance …

## Example

The following simple example shows how to find the center of a circle of known radius to to best fit observed 2D points. It is a simplified version of one of the JUnit test cases. In the complete test case, both the circle center and its radius are fitted, here the radius is fixed. From the optimizer’s perspective the parameters are the location (x, y) of the center of the circle, the observed values are all the assumed radius of the circle: 70.0 and the computed values are the distance from each of the observedPoints to center of the circle (as given by the current value of the parameters).

  final double radius = 70.0;
final Vector2D[] observedPoints = new Vector2D[] {
new Vector2D( 30.0,  68.0),
new Vector2D( 50.0,  -6.0),
new Vector2D(110.0, -20.0),
new Vector2D( 35.0,  15.0),
new Vector2D( 45.0,  97.0)
};

// the model function components are the distances to current estimated center,
// they should be as close as possible to the specified radius
MultivariateJacobianFunction distancesToCurrentCenter = new MultivariateJacobianFunction() {
public Pair&lt;RealVector, RealMatrix&gt; value(final RealVector point) {

Vector2D center = new Vector2D(point.getEntry(0), point.getEntry(1));

RealVector value = new ArrayRealVector(observedPoints.length);
RealMatrix jacobian = new Array2DRowRealMatrix(observedPoints.length, 2);

for (int i = 0; i &lt; observedPoints.length; ++i) {
Vector2D o = observedPoints[i];
Vector2D diff = center.subtract(o);
double modelI = diff.getNorm();
value.setEntry(i, modelI);
// derivative with respect to p0 = x center
jacobian.setEntry(i, 0, diff.getX() / modelI);
// derivative with respect to p1 = y center
jacobian.setEntry(i, 1, diff.getY() / modelI);
}

return new Pair&lt;RealVector, RealMatrix&gt;(value, jacobian);

}
};

// the target is to have all points at the specified radius from the center
double[] prescribedDistances = new double[observedPoints.length];

// least squares problem to solve : modeled radius should be close to target radius
LeastSquaresProblem problem = new LeastSquaresBuilder().
start(new double[] { 100.0, 50.0 }).
model(distancesToCurrentCenter).
target(prescribedDistances).
lazyEvaluation(false).
maxEvaluations(1000).
maxIterations(1000).
build();
LeastSquaresOptimizer.Optimum optimum = new LevenbergMarquardtOptimizer().optimize(problem);
Vector2D fittedCenter = new Vector2D(optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1));
System.out.println("fitted center: " + fittedCenter.getX() + " " + fittedCenter.getY());
System.out.println("RMS: "           + optimum.getRMS());
System.out.println("evaluations: "   + optimum.getEvaluations());
System.out.println("iterations: "    + optimum.getIterations());


## References

[1] Crassidis, John L., and John L. Junkins. Optimal estimation of dynamic systems. Boca Raton, FL: CRC Press, 2012.