nlopt4j - NLopt for Java

JNI wrapper for NLopt (http://ab-initio.mit.edu/wiki/index.php/NLopt).

Example

The following example is a port of the sample used in NLopt tutorial.

static final class Constraint implements NLopt.NLopt_func {
    double a, b;
    Constraint(double a, double b) {
        this.a = a;
        this.b = b;
    }
    @Override
    public double execute(double[] x, double[] gradient) {
        double a = this.a, b = this.b;
        if (gradient.length == x.length) {
            gradient[0] = 3.0 * a * (a*x[0] + b) * (a*x[0] + b);
            gradient[1] = -1.0;
        }
        return ((a*x[0] + b) * (a*x[0] + b) * (a*x[0] + b) - x[1]);
    }
}

@Test
public void testTutorialSample() {
    NLopt.NLopt_func func = new NLopt.NLopt_func() {
        @Override
        public double execute(double[] x, double[] gradient) {
            if (gradient.length == x.length) {
                gradient[0] = 0.0;
                gradient[1] = 0.5 / Math.sqrt(x[1]);
            }
            return Math.sqrt(x[1]);
        }
    };

    double[] lb = {Double.NEGATIVE_INFINITY, 0}; /* lower bounds */
    NLopt optimizer = new NLopt(NLopt.NLOPT_LD_MMA, 2);
    optimizer.setLowerBounds(lb);
    optimizer.setMinObjective(func);
    optimizer.addInequalityConstraint(new Constraint(2.0, 0.0), 1e-8);
    optimizer.addInequalityConstraint(new Constraint(-1.0, 1.0), 1e-8);
    optimizer.setRelativeToleranceOnX(1e-4);
    double[] x = {1.234, 5.678};  /* some initial guess */
    NLoptResult minf = optimizer.optimize(x);
    System.out.println(String.format("found minimum at f(%f,%f) = %f\n", x[0], x[1], minf.minValue()));
    assertEquals(0.544331, minf.minValue(), 1e-4);
}

Build Instructions

Currently build has been tested on Windows and Mac OS X (Snow Leopard).

32-bit Windows

Requires following:

• Visual C++ 2013 (should work with older versions but not tested)
• CMake 2.8.12 or above.
• Java 1.6 or above.
• Maven 2.1 or above.

Currently the NLopt libraries and headers must be installed to c:/nlopt. The NLopt include files must be under c:/nlopt/include. The NLopt library files must be under c:/nlopt/lib.

To build follow these steps from a VS 2013 command shell:

cmake -G "Visual Studio 12"

Above creates VS solution files which can be opened in the IDE. Build the solution inside the IDE.

To build and test the Java bits, run following from the command shell.

mvn test

Note that on Windows the required DLLs must be on the PATH.

Mac OS X

Requires following:

• CMake 2.8.12 or above.
• GCC 4.2 or above.
• Java 1.6 or above.
• Maven 2.1 or above.

Currently the NLopt libraries and headers must be installed to ~/nlopt. The NLopt include files must be under ~/nlopt/include. The NLopt library files must be under ~/nlopt/lib.

To build follow these steps:

cmake -G "Unix Makefiles"
make
mvn test

Reference

The main Java class is org.nlopt4j.optimizer.NLopt.

An instance of NLopt encapsulates the NLopt handle of the native library.

The NLopt constructor is defined as:

NLopt(int algorithm, int dim)

The algorithm is an integer constants such as NLopt.NLOPT_GN_DIRECT. The dim argument specifies the number of variables in the problem.

The NLopt instance can be destroyed by calling:

void release()

This destroys the underlying native object. I recommend calling this in the finally block. Note that release() will be invoked by the finalizer as well, calling release() multiple times has no effect.

Objective function

The objective function must implement the following interface.

class NLopt {
    interface NLopt_func {
        double execute(double[] x, double[] gradient);
    }
}

The x array contains the values of the variables - the objective function must return the evaluated result using these parameters. If the gradient array length is equal to the x array length, then the objective function must compute the first order partial derivative for each x and store in the gradient array.

If the objective function throws an exception the evaluations will be terminated.

Example:

NLopt.NLopt_func func = new NLopt.NLopt_func() {
    @Override
    public double execute(double[] x, double[] gradient) {
        if (gradient.length == x.length) {
            gradient[0] = 0.0;
            gradient[1] = 0.5 / Math.sqrt(x[1]);
        }
        return Math.sqrt(x[1]);
    }
};

Running optimizer

The method for running the optimizer has following signature:

NLoptResult optimize(double[] x)

NLoptResult is an object that provides access to the final value of the objective function, and the reason for termination.

Methods

Set bounds.

void setUpperBounds(double[] ub)
void setLowerBounds(double[] lb)

Set maximum number of evaluations.

void setMaxEval(int n)

Set stop value.

void setStopValue(double v)

Set maximum time in seconds.

void setMaxTime(double t)

Set objective functions.

void setMinObjective(NLopt_func func) 
void setMaxObjective(NLopt_func func) 

Add equality / inequality constraints.

void addInequalityConstraint(NLopt_func func, double tol) 
void addEqualityConstraint(NLopt_func func, double tol)

Set relative tolerance.

void setRelativeToleranceOnX(double tol)

Get the list of algorithms.

static String[] getGlobalAlgorithms()
static String[] getLocalAlgorithms() 

Convert from algorithm name to integer code.

static int getAlgorithmCode(String name)

Get the text for a return code.

static String getSuccessDesc(int code)
static String getErrorDesc(int code)