Solving your first model in Java

LocalSolver is implemented in C++ language. Nevertheless, object-oriented APIs are provided for Java 7.0 (or superior), allowing a full integration of LocalSolver in your Java business applications. LocalSolver’s APIs are lightweight, with only a few classes to manipulate. Note that LocalSolver is a model-and-run math programming solver: having instantiated the model, no additional code has to be written in order to run the solver.

In this section, we show you how to model and solve your first problem in Java: the optimization of the shape of a bucket. With a limited surface of material (S=π), we try to build a bucket that holds the largest volume.

This small example is more precisely described in our example tour. Here our main goal is to learn how to write and launch a model.

Writing the model

Below is the Java program which models this non linear problem (see examples/optimal_bucket).

import localsolver.*;

public class OptimalBucket {
    private static final double PI = 3.14159265359;

    // LocalSolver
    private final LocalSolver localsolver;

    // LS Program variables
    private LSExpression R;
    private LSExpression r;
    private LSExpression h;

    private LSExpression surface;
    private LSExpression volume;

    private OptimalBucket(LocalSolver localsolver) {
        this.localsolver = localsolver;

    private void solve(int limit) {
        // Declare the optimization model
        LSModel model = localsolver.getModel();
        LSExpression piConst = model.createConstant(PI);

        // Numerical decisions
        R = model.floatVar(0, 1);
        r = model.floatVar(0, 1);
        h = model.floatVar(0, 1);

        // surface = PI*r^2 + PI*(R+r)*sqrt((R - r)^2 + h^2)
        LSExpression s1 =, r, r);
        LSExpression s2 = model.pow(model.sub(R, r), 2);
        LSExpression s3 = model.pow(h, 2);
        LSExpression s4 = model.sqrt(model.sum(s2, s3));
        LSExpression s5 = model.sum(R, r);
        LSExpression s6 =, s5, s4);
        surface = model.sum(s1, s6);

        // Surface must not exceed the surface of the plain disc
        model.addConstraint(model.leq(surface, PI));

        LSExpression v1 = model.pow(R, 2);
        LSExpression v2 =, r);
        LSExpression v3 = model.pow(r, 2);

        // volume = PI*h/3*(R^2 + R*r + r^2)
        volume =, model.div(h, 3), model.sum(v1, v2, v3));

        // Maximize the volume


        // Parametrize the solver


    /* Write the solution in a file with the following format:
     * - surface and volume of the bucket
     * - values of R, r and h */
    private void writeSolution(String fileName) throws IOException {
        try (PrintWriter output = new PrintWriter(fileName)) {
            output.println(surface.getDoubleValue() + " " + volume.getDoubleValue());
            output.println(R.getDoubleValue() + " " + r.getDoubleValue() + " " + h.getDoubleValue());

    public static void main(String[] args) {

        String outputFile = args.length > 0 ? args[0] : null;
        String strTimeLimit = args.length > 1 ? args[1] : "2";

        try (LocalSolver localsolver = new LocalSolver()) {
            OptimalBucket model = new OptimalBucket(localsolver);
            if (outputFile != null) {
        } catch (Exception ex) {

After creating the LocalSolver environment LocalSolver(), all the decision variables of the model, are declared with function floatVar() (or also boolVar(), intVar(), setVar(), listVar()). Intermediate expressions can be built upon these decision variables by using other operators or functions. For example, in the model above: power (pow), square root (sqrt), less than or equal to (leq). Many other mathematical operators are available, allowing you to model and solve highly-nonlinear combinatorial optimization problems. The functions constraint or maximize are used for tagging expressions as constrained or maximized.

Compiling and running the Java program

For compiling, Java Development Kit 8.0 (or superior) must be installed on your computer. On Windows, the above program is compiled and launched using the following lines:

javac -cp %LS_HOME%\bin\localsolver.jar
java -cp %LS_HOME%\bin\localsolver.jar;. -Djava.library.path=%LS_HOME%\bin\ OptimalBucket

Note that on Windows, in a PowerShell window you would use the following lines:

javac -cp $env:LS_HOME\bin\localsolver.jar
java "-Djava.library.path=$env:LS_HOME\bin\" -cp "$env:LS_HOME\bin\localsolver.jar;." OptimalBucket

On Linux or Mac OS, the above program is compiled and launched using the following lines:

javac -cp /opt/localsolver_12_5/bin/localsolver.jar
java -cp /opt/localsolver_12_5/bin/localsolver.jar:. -Djava.library.path=/opt/localsolver_12_5/bin/ OptimalBucket

Then, the following trace will appear in your console:

LocalSolver 9.5.20200409-Win64. All rights reserved.
Load .\optimal_bucket.lsp...
Run model...
Run param...
Run solver...

Model:  expressions = 26, decisions = 3, constraints = 1, objectives = 1
Param:  time limit = 2 sec, no iteration limit

[objective direction ]:     maximize

[  0 sec,       0 itr]:            0
[ optimality gap     ]:         100%
[  0 sec,   42898 itr]:      0.68709
[ optimality gap     ]:      < 0.01%

42898 iterations performed in 0 seconds

Optimal solution:
  obj    =      0.68709
  gap    =      < 0.01%
  bounds =     0.687189

If no time limit is set, the search will continue until optimality is proven (Optimal solution message) or until you force the stop of the program by pressing Ctrl+C. The trace in console starts with the key figures of the model: number of expressions, decisions, constraints and objectives.

Once the search is finished, the total number of iterations and the elapsed time are displayed, as well as the status and the value of the best solution found. The solution status can be Inconsistent, Infeasible, Feasible or Optimal.

If you have trouble compiling or launching the program, please have a look at the Installation & licensing. We invite users willing to go further with APIs to consult the Java API Reference.