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服务器之家 - 编程语言 - Java教程 - 使用java写的矩阵乘法实例(Strassen算法)

使用java写的矩阵乘法实例(Strassen算法)

2021-08-04 09:50Jack_Weng Java教程

这篇文章主要给大家介绍了关于如何使用java写的矩阵乘法(Strassen算法)的相关资料,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧

Strassen算法于1969年由德国数学家Strassen提出,该方法引入七个中间变量,每个中间变量都只需要进行一次乘法运算。而朴素算法却需要进行8次乘法运算。

原理

 

Strassen算法的原理如下所示,使用sympy验证Strassen算法的正确性

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import sympy as s
 
A = s.Symbol("A")
B = s.Symbol("B")
C = s.Symbol("C")
D = s.Symbol("D")
E = s.Symbol("E")
F = s.Symbol("F")
G = s.Symbol("G")
H = s.Symbol("H")
p1 = A * (F - H)
p2 = (A + B) * H
p3 = (C + D) * E
p4 = D * (G - E)
p5 = (A + D) * (E + H)
p6 = (B - D) * (G + H)
p7 = (A - C) * (E + F)
 
print(A * E + B * G, (p5 + p4 - p2 + p6).simplify())
print(A * F + B * H, (p1 + p2).simplify())
print(C * E + D * G, (p3 + p4).simplify())
print(C * F + D * H, (p1 + p5 - p3 - p7).simplify())

复杂度分析

$$f(N)=7\times f(\frac{N}{2})=7^2\times f(\frac{N}{4})=...=7^k\times f(\frac{N}{2^k})$$

最终复杂度为$7^{log_2 N}=N^{log_2 7}$

java矩阵乘法(Strassen算法)

 

代码如下,可以看看数据结构的定义,时间换空间。

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public class Matrix {
    private final Matrix[] _matrixArray;
    private final int n;
    private int element;
    public Matrix(int n) {
        this.n = n;
        if (n != 1) {
            this._matrixArray = new Matrix[4];
            for (int i = 0; i < 4; i++) {
                this._matrixArray[i] = new Matrix(n / 2);
            }
        } else {
            this._matrixArray = null;
        }
    }
    private Matrix(int n, boolean needInit) {
        this.n = n;
        if (n != 1) {
            this._matrixArray = new Matrix[4];
        } else {
            this._matrixArray = null;
        }
    }
    public void set(int i, int j, int a) {
        if (n == 1) {
            element = a;
        } else {
            int size = n / 2;
            this._matrixArray[(i / size) * 2 + (j / size)].set(i % size, j % size, a);
        }
    }
    public Matrix multi(Matrix m) {
        Matrix result = null;
        if (n == 1) {
            result = new Matrix(1);
            result.set(0, 0, (element * m.element));
        } else {
            result = new Matrix(n, false);
            result._matrixArray[0] = P5(m).add(P4(m)).minus(P2(m)).add(P6(m));
            result._matrixArray[1] = P1(m).add(P2(m));
            result._matrixArray[2] = P3(m).add(P4(m));
            result._matrixArray[3] = P5(m).add(P1(m)).minus(P3(m)).minus(P7(m));
        }
        return result;
    }
    public Matrix add(Matrix m) {
        Matrix result = null;
        if (n == 1) {
            result = new Matrix(1);
            result.set(0, 0, (element + m.element));
        } else {
            result = new Matrix(n, false);
            result._matrixArray[0] = this._matrixArray[0].add(m._matrixArray[0]);
            result._matrixArray[1] = this._matrixArray[1].add(m._matrixArray[1]);
            result._matrixArray[2] = this._matrixArray[2].add(m._matrixArray[2]);
            result._matrixArray[3] = this._matrixArray[3].add(m._matrixArray[3]);;
        }
        return result;
    }
    public Matrix minus(Matrix m) {
        Matrix result = null;
        if (n == 1) {
            result = new Matrix(1);
            result.set(0, 0, (element - m.element));
        } else {
            result = new Matrix(n, false);
            result._matrixArray[0] = this._matrixArray[0].minus(m._matrixArray[0]);
            result._matrixArray[1] = this._matrixArray[1].minus(m._matrixArray[1]);
            result._matrixArray[2] = this._matrixArray[2].minus(m._matrixArray[2]);
            result._matrixArray[3] = this._matrixArray[3].minus(m._matrixArray[3]);;
        }
        return result;
    }
    protected Matrix P1(Matrix m) {
        return _matrixArray[0].multi(m._matrixArray[1]).minus(_matrixArray[0].multi(m._matrixArray[3]));
    }
    protected Matrix P2(Matrix m) {
        return _matrixArray[0].multi(m._matrixArray[3]).add(_matrixArray[1].multi(m._matrixArray[3]));
    }
    protected Matrix P3(Matrix m) {
        return _matrixArray[2].multi(m._matrixArray[0]).add(_matrixArray[3].multi(m._matrixArray[0]));
    }
    protected Matrix P4(Matrix m) {
        return _matrixArray[3].multi(m._matrixArray[2]).minus(_matrixArray[3].multi(m._matrixArray[0]));
    }
    protected Matrix P5(Matrix m) {
        return (_matrixArray[0].add(_matrixArray[3])).multi(m._matrixArray[0].add(m._matrixArray[3]));
    }
    protected Matrix P6(Matrix m) {
        return (_matrixArray[1].minus(_matrixArray[3])).multi(m._matrixArray[2].add(m._matrixArray[3]));
    }
    protected Matrix P7(Matrix m) {
        return (_matrixArray[0].minus(_matrixArray[2])).multi(m._matrixArray[0].add(m._matrixArray[1]));
    }
    public int get(int i, int j) {
        if (n == 1) {
            return element;
        } else {
            int size = n / 2;
            return this._matrixArray[(i / size) * 2 + (j / size)].get(i % size, j % size);
        }
    }
    public void display() {
        for (int i = 0; i < n; i++) {
            for (int j = 0; j < n; j++) {
                System.out.print(get(i, j));
                System.out.print(" ");
            }
            System.out.println();
        }
    }
    
    public static void main(String[] args) {
        Matrix m = new Matrix(2);
        Matrix n = new Matrix(2);
        m.set(0, 0, 1);
        m.set(0, 1, 3);
        m.set(1, 0, 5);
        m.set(1, 1, 7);
        n.set(0, 0, 8);
        n.set(0, 1, 4);
        n.set(1, 0, 6);
        n.set(1, 1, 2);
        Matrix res = m.multi(n);
        res.display();
    }
 
}

总结

 

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原文链接:https://blog.csdn.net/wj310298/article/details/44857175

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