Task: Element-wise operations
Go: package element

import (
    "fmt"
    "math"
)

type Matrix struct {
    ele    []float64
    stride int
}

func MatrixFromRows(rows [][]float64) Matrix {
    if len(rows) == 0 {
        return Matrix{nil, 0}
    }
    m := Matrix{make([]float64, len(rows)*len(rows[0])), len(rows[0])}
    for rx, row := range rows {
        copy(m.ele[rx*m.stride:(rx+1)*m.stride], row)
    }
    return m
}

func like(m Matrix) Matrix {
    return Matrix{make([]float64, len(m.ele)), m.stride}
}

func (m Matrix) String() string {
    s := ""
    for e := 0; e < len(m.ele); e += m.stride {
        s += fmt.Sprintf("%6.3f \n", m.ele[e:e+m.stride])
    }
    return s
}

type binaryFunc64 func(float64, float64) float64

func elementWiseMM(m1, m2 Matrix, f binaryFunc64) Matrix {
    z := like(m1)
    for i, m1e := range m1.ele {
        z.ele[i] = f(m1e, m2.ele[i])
    }
    return z
}

func elementWiseMS(m Matrix, s float64, f binaryFunc64) Matrix {
    z := like(m)
    for i, e := range m.ele {
        z.ele[i] = f(e, s)
    }
    return z
}

func add(a, b float64) float64 { return a + b }
func sub(a, b float64) float64 { return a - b }
func mul(a, b float64) float64 { return a * b }
func div(a, b float64) float64 { return a / b }
func exp(a, b float64) float64 { return math.Pow(a, b) }

func AddMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, add) }
func SubMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, sub) }
func MulMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, mul) }
func DivMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, div) }
func ExpMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, exp) }

func AddScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, add) }
func SubScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, sub) }
func MulScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, mul) }
func DivScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, div) }
func ExpScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, exp) }

Java: import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.function.BiFunction;
import java.util.stream.Stream;

@SuppressWarnings("serial")
public class ElementWiseOp {
	static final Map<String, BiFunction<Double, Double, Double>> OPERATIONS = new HashMap<String, BiFunction<Double, Double, Double>>() {
		{
			put("add", (a, b) -> a + b);
			put("sub", (a, b) -> a - b);
			put("mul", (a, b) -> a * b);
			put("div", (a, b) -> a / b);
			put("pow", (a, b) -> Math.pow(a, b));
			put("mod", (a, b) -> a % b);
		}
	};
	public static Double[][] scalarOp(String op, Double[][] matr, Double scalar) {
		BiFunction<Double, Double, Double> operation = OPERATIONS.getOrDefault(op, (a, b) -> a);
		Double[][] result = new Double[matr.length][matr[0].length];
		for (int i = 0; i < matr.length; i++) {
			for (int j = 0; j < matr[i].length; j++) {
				result[i][j] = operation.apply(matr[i][j], scalar);
			}
		}
		return result;
	}
	public static Double[][] matrOp(String op, Double[][] matr, Double[][] scalar) {
		BiFunction<Double, Double, Double> operation = OPERATIONS.getOrDefault(op, (a, b) -> a);
		Double[][] result = new Double[matr.length][Stream.of(matr).mapToInt(a -> a.length).max().getAsInt()];
		for (int i = 0; i < matr.length; i++) {
			for (int j = 0; j < matr[i].length; j++) {
				result[i][j] = operation.apply(matr[i][j], scalar[i % scalar.length][j
						% scalar[i % scalar.length].length]);
			}
		}
		return result;
	}
	public static void printMatrix(Double[][] matr) {
		Stream.of(matr).map(Arrays::toString).forEach(System.out::println);
	}
	public static void main(String[] args) {
		printMatrix(scalarOp("mul", new Double[][] {
				{ 1.0, 2.0, 3.0 }, 
				{ 4.0, 5.0, 6.0 }, 
				{ 7.0, 8.0, 9.0 }
		}, 3.0));

		printMatrix(matrOp("div", new Double[][] {
				{ 1.0, 2.0, 3.0 }, 
				{ 4.0, 5.0, 6.0 }, 
				{ 7.0, 8.0, 9.0 }
		}, new Double[][] {
				{ 1.0, 2.0}, 
				{ 3.0, 4.0} 
		}));
	}
}