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使用java寫(xiě)的矩陣乘法實(shí)例（Strassen算法）

更新時(shí)間：2021年02月04日 13:20:21 作者：Jack_Weng

這篇文章主要給大家介紹了關(guān)于如何使用java寫(xiě)的矩陣乘法（Strassen算法）的相關(guān)資料，文中通過(guò)示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來(lái)一起學(xué)習(xí)學(xué)習(xí)吧

Strassen算法于1969年由德國(guó)數(shù)學(xué)家Strassen提出，該方法引入七個(gè)中間變量，每個(gè)中間變量都只需要進(jìn)行一次乘法運(yùn)算。而樸素算法卻需要進(jìn)行8次乘法運(yùn)算。

原理

Strassen算法的原理如下所示，使用sympy驗(yàn)證Strassen算法的正確性

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())

復(fù)雜度分析

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

最終復(fù)雜度為$7^{log_2 N}=N^{log_2 7}$

java矩陣乘法（Strassen算法）

代碼如下，可以看看數(shù)據(jù)結(jié)構(gòu)的定義，時(shí)間換空間。

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();
	}
 
}