ScalarSolver

java.lang.Object
- wsh.opt.ScalarSolver

```
public class ScalarSolver
extends java.lang.Object
```
Search a single variable for a value that minimizes a function

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static interface ScalarSolver.Function
Implement a function of one variable to be minimized

Constructor Summary

Constructors
Constructor Description

ScalarSolver(ScalarSolver.Function function)
Constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`solve(double scalarMin, double scalarMax, double okError, double okFraction, int numberIterations, Monitor monitor)`	Minimize a function of scalar and return the optimum value.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - ScalarSolver
```
public ScalarSolver(ScalarSolver.Function function)
```
    Constructor
    
    Parameters:
    
    function - Objective function to be minimized.
- Method Detail
  - solve
```
public double solve(double scalarMin,
                    double scalarMax,
                    double okError,
                    double okFraction,
                    int numberIterations,
                    Monitor monitor)
```
    Minimize a function of scalar and return the optimum value.
    
    Parameters:
    
    scalarMin - The minimum value allowed for the argument scalar.
    
    scalarMax - The maximum value allowed for the argument scalar.
    
    okError - The unknown error in scalar should be less than this fraction of the range: dscalar/(scalarMax-scalarMin) <= okError, where dscalar is the error bound on the returned value of scalar.
    
    okFraction - The error in scalar should be less than this fraction of the minimum possible range for scalar: dscalar <= okFraction*(scalar-scalarMin), where dscalar is the error bound on the returned value of scalar.
    
    numberIterations - The maximum number of iterations if greater than 6. The optimization performs at least 6 iterations -- the minimum necessary for a genuinely parabolic function. I recommend at least 20.
    
    monitor - For reporting progress. Ignored if null.
    
    Returns:
    
    The optimum value minimizing the function.