Fixed typo

src/main/java/ExampleGradientDescent.java

@@ -0,0 +1,16 @@
+import org.ejml.simple.SimpleMatrix;
+
+import java.util.function.Function;
+
+public class ExampleGradientDescent {
+    public static void main(String[] args) {
+        GradientDescent gd = new GradientDescent();
+
+        Function<Double, Double> f = x -> x*x;
+        System.out.println(gd.findLocalMinimum(f, 1));
+
+        Function<SimpleMatrix, SimpleMatrix> g = x -> x.elementMult(x);
+        SimpleMatrix initialX = new SimpleMatrix(2, 1, true, new double[]{1, 0.5});
+        System.out.println(gd.findLocalMinimum(g, initialX));
+    }
+}

src/main/java/GradientDescent.java

@@ -1,37 +1,104 @@
+import org.ejml.simple.SimpleMatrix;
+
 import java.util.function.Function;
 
 public class GradientDescent {
+    private static final double STANDARD_PRECISION = 0.000001;
+    private static final double STANDARD_STEP_COEFFICIENT = 0.5;
+    private static final int STANDARD_MAX_ITERATIONS = 1000;
 
-    private final double precision = 0.000001;
+    private double precision;
+    private double stepCoefficient;
 
-    public double findLocalMinimum(Function<Double, Double> f, double initialX) {
-        double stepCoefficient = 0.5;
+    public GradientDescent(double precision, double stepCoefficient) {
+        this.precision = precision;
+        this.stepCoefficient = stepCoefficient;
+    }
+
+    public GradientDescent() {
+        this(STANDARD_PRECISION, STANDARD_STEP_COEFFICIENT);
+    }
+
+    /**
+     * Performs gradient descent on a function f: ℝ -> ℝ
+     * @param f real-valued function
+     * @param initialX initial X vector
+     * @param maxIterations maximum number of iterations
+     * @return approximation of the nearest local minimum
+     */
+    public double findLocalMinimum(Function<Double, Double> f, double initialX, int maxIterations) {
         double previousStep = 1.0;
         double currentX = initialX;
         double previousX = initialX;
         double previousY = f.apply(previousX);
-        int iter = 1000;
 
-        currentX += stepCoefficient * previousY;
+        currentX += this.stepCoefficient * previousY;
 
-        while (previousStep > precision && iter > 0) {
-            iter--;
+        while (previousStep > this.precision && maxIterations > 0) {
+            maxIterations--;
             double currentY = f.apply(currentX);
             if (currentY > previousY) {
-                stepCoefficient = -stepCoefficient / 2;
+                this.stepCoefficient = -this.stepCoefficient / 2;
             }
             previousX = currentX;
-            currentX += stepCoefficient * previousY;
+            currentX += this.stepCoefficient * previousY;
             previousY = currentY;
             previousStep = StrictMath.abs(currentX - previousX);
         }
         return currentX;
     }
 
-    public static void main(String[] args) {
-        GradientDescent gd = new GradientDescent();
-        Function<Double, Double> f = x -> x*x;
+    public double findLocalMinimum(Function<Double, Double> f, double initialX) {
+        return findLocalMinimum(f, initialX, STANDARD_MAX_ITERATIONS);
+    }
 
-        System.out.println(gd.findLocalMinimum(f, 1));
+    /**
+     * Performs gradient descent on a function f: ℝⁿ -> ℝⁿ.
+     * @param f vector-valued function
+     * @param initialX initial X vector
+     * @param maxIterations maximum number of iterations
+     * @return approximation of the nearest local minimum
+     */
+    public SimpleMatrix findLocalMinimum(Function<SimpleMatrix, SimpleMatrix> f,
+                                         SimpleMatrix initialX, int maxIterations) {
+        double previousStep = 1.0;
+        SimpleMatrix currentX = initialX;
+        SimpleMatrix previousX = initialX;
+        SimpleMatrix previousY = f.apply(previousX);
+
+        currentX = currentX.plus(this.stepCoefficient, previousY);
+
+        while (previousStep > this.precision && maxIterations > 0) {
+            maxIterations--;
+            SimpleMatrix currentY = f.apply(currentX);
+            if (currentY.normF() > previousY.normF()) {
+                this.stepCoefficient = -this.stepCoefficient / 2;
+            }
+            previousX = currentX;
+            currentX = currentX.plus(this.stepCoefficient, previousY);
+            previousY = currentY;
+            previousStep = currentX.minus(previousX).normF();
+        }
+        return currentX;
+    }
+
+    public SimpleMatrix findLocalMinimum(Function<SimpleMatrix, SimpleMatrix> f, SimpleMatrix initialX) {
+        return findLocalMinimum(f, initialX, STANDARD_MAX_ITERATIONS);
+    }
+
+    public double getPrecision() {
+        return precision;
+    }
+
+    public void setPrecision(double precision) {
+        this.precision = precision <= 0 ? STANDARD_PRECISION : precision;
+    }
+
+    public double getStepCoefficient() {
+        return stepCoefficient;
+    }
+
+    public void setStepCoefficient(double stepCoefficient) {
+        this.stepCoefficient = stepCoefficient;
     }
 }