Eigen学习记录
Eigen学习记录
Eigen库由Ubuntu软件源中提供，通过apt命令可以很方便的安装Eigen。
sudo apt-get install libeigen3-devEigen与其他库不同，它是一个由头文件搭建起来的库，Eigen头文件的默认安装位置在“/usr/include/eigen3/”中。我们在使用时，只需引入Eigen头文件，不需要链接它的库文件，在CMakeLists.txt里添加Eigen头文件的目录。
#添加头文件
include_directories("/usr/include/eigen3")eigen.cpp
#include <iostream>
#include <Eigen/Dense>
using Eigen::MatrixXd;//using namespace Eigen
int main()
{
MatrixXd m(2,2);
m(0,0) = 3;
m(1,0) = 2.5;
m(0,1) = -1;
m(1,1) = m(1,0) + m(0,1);
std::cout << m << std::endl;
}CMakeLists.txt
projiect(eigen) inlcude_directories("/usr/include/eigen3") add_executable(eigen eigen.cpp)When you run the program, it produces the following output:
3 -1
2.5 1.5The Matrix class
The three mandatory template parameters of Matrix are:
Matrix<typename Scalar, int RowsAtCompileTime, int ColsAtCompileTime>
//Matrix4f is a 4x4 matrix of floats.
typedef Matrix<float, 4, 4> Matrix4f;
Vectors
typedef Matrix<folat,3,1>Vector3f;//列向量，列为1.
typedef Matrix<int,1,2>RowVector2i;//行向量，行为1,2列。和MATLAB对比：http://eigen.tuxfamily.org/dox/AsciiQuickReference.txt
参考博客：https://www.cnblogs.com/python27/p/EigenQuickRef.html
Eigen 矩阵定义
#include <Eigen/Dense>
Matrix<double, 3, 3> A; // Fixed rows and cols. Same as Matrix3d.
Matrix<double, 3, Dynamic> B; // Fixed rows, dynamic cols.
Matrix<double, Dynamic, Dynamic> C; // Full dynamic. Same as MatrixXd.
Matrix<double, 3, 3, RowMajor> E; // Row major; default is column-major.
Matrix3f P, Q, R; // 3x3 float matrix.
Vector3f x, y, z; // 3x1 float matrix.
RowVector3f a, b, c; // 1x3 float matrix.
VectorXd v; // Dynamic column vector of doubles
// Eigen // Matlab // comments
x.size() // length(x) // vector size
C.rows() // size(C,1) // number of rows
C.cols() // size(C,2) // number of columns
x(i) // x(i+1) // Matlab is 1-based
C(i,j) // C(i+1,j+1) //Eigen 基础使用
// Basic usage
// Eigen // Matlab // comments
x.size() // length(x) // vector size
C.rows() // size(C,1) // number of rows
C.cols() // size(C,2) // number of columns
x(i) // x(i+1) // Matlab is 1-based
C(i, j) // C(i+1,j+1) //
A.resize(4, 4); // Runtime error if assertions are on.
B.resize(4, 9); // Runtime error if assertions are on.
A.resize(3, 3); // Ok; size didn't change.
B.resize(3, 9); // Ok; only dynamic cols changed.
A << 1, 2, 3, // Initialize A. The elements can also be
4, 5, 6, // matrices, which are stacked along cols
7, 8, 9; // and then the rows are stacked.
B << A, A, A; // B is three horizontally stacked A's.
A.fill(10); // Fill A with all 10's.Eigen 特殊矩阵生成
// Eigen // Matlab
MatrixXd::Identity(rows,cols) // eye(rows,cols)
C.setIdentity(rows,cols) // C = eye(rows,cols)
MatrixXd::Zero(rows,cols) // zeros(rows,cols)
C.setZero(rows,cols) // C = ones(rows,cols)
MatrixXd::Ones(rows,cols) // ones(rows,cols)
C.setOnes(rows,cols) // C = ones(rows,cols)
MatrixXd::Random(rows,cols) // rand(rows,cols)*2-1 // MatrixXd::Random returns uniform random numbers in (-1, 1).
C.setRandom(rows,cols) // C = rand(rows,cols)*2-1
VectorXd::LinSpaced(size,low,high) // linspace(low,high,size)'
v.setLinSpaced(size,low,high) // v = linspace(low,high,size)'Eigen 矩阵分块
// Matrix slicing and blocks. All expressions listed here are read/write.
// Templated size versions are faster. Note that Matlab is 1-based (a size N
// vector is x(1)...x(N)).
// Eigen // Matlab
x.head(n) // x(1:n)
x.head<n>() // x(1:n)
x.tail(n) // x(end - n + 1: end)
x.tail<n>() // x(end - n + 1: end)
x.segment(i, n) // x(i+1 : i+n)
x.segment<n>(i) // x(i+1 : i+n)
P.block(i, j, rows, cols) // P(i+1 : i+rows, j+1 : j+cols)
P.block<rows, cols>(i, j) // P(i+1 : i+rows, j+1 : j+cols)
P.row(i) // P(i+1, :)
P.col(j) // P(:, j+1)
P.leftCols<cols>() // P(:, 1:cols)
P.leftCols(cols) // P(:, 1:cols)
P.middleCols<cols>(j) // P(:, j+1:j+cols)
P.middleCols(j, cols) // P(:, j+1:j+cols)
P.rightCols<cols>() // P(:, end-cols+1:end)
P.rightCols(cols) // P(:, end-cols+1:end)
P.topRows<rows>() // P(1:rows, :)
P.topRows(rows) // P(1:rows, :)
P.middleRows<rows>(i) // P(i+1:i+rows, :)
P.middleRows(i, rows) // P(i+1:i+rows, :)
P.bottomRows<rows>() // P(end-rows+1:end, :)
P.bottomRows(rows) // P(end-rows+1:end, :)
P.topLeftCorner(rows, cols) // P(1:rows, 1:cols)
P.topRightCorner(rows, cols) // P(1:rows, end-cols+1:end)
P.bottomLeftCorner(rows, cols) // P(end-rows+1:end, 1:cols)
P.bottomRightCorner(rows, cols) // P(end-rows+1:end, end-cols+1:end)
P.topLeftCorner<rows,cols>() // P(1:rows, 1:cols)
P.topRightCorner<rows,cols>() // P(1:rows, end-cols+1:end)
P.bottomLeftCorner<rows,cols>() // P(end-rows+1:end, 1:cols)
P.bottomRightCorner<rows,cols>() // P(end-rows+1:end, end-cols+1:end)Eigen 矩阵元素交换
// Of particular note is Eigen's swap function which is highly optimized.
// Eigen // Matlab
R.row(i) = P.col(j); // R(i, :) = P(:, i)
R.col(j1).swap(mat1.col(j2)); // R(:, [j1 j2]) = R(:, [j2, j1])Eigen 矩阵转置
// Views, transpose, etc; all read-write except for .adjoint(). // Eigen // Matlab
R.adjoint() // R'
R.transpose() // R.' or conj(R')
R.diagonal() // diag(R)
x.asDiagonal() // diag(x)
R.transpose().colwise().reverse(); // rot90(R)
R.conjugate() // conj(R)Eigen 矩阵乘积
// All the same as Matlab, but matlab doesn't have *= style operators.
// Matrix-vector. Matrix-matrix. Matrix-scalar.
y = M*x; R = P*Q; R = P*s;
a = b*M; R = P - Q; R = s*P;
a *= M; R = P + Q; R = P/s;
R *= Q; R = s*P;
R += Q; R *= s;
R -= Q; R /= s;Eigen 矩阵单个元素操作
// Vectorized operations on each element independently
// Eigen // Matlab
R = P.cwiseProduct(Q); // R = P .* Q
R = P.array() * s.array();// R = P .* s
R = P.cwiseQuotient(Q); // R = P ./ Q
R = P.array() / Q.array();// R = P ./ Q
R = P.array() + s.array();// R = P + s
R = P.array() - s.array();// R = P - s
R.array() += s; // R = R + s
R.array() -= s; // R = R - s
R.array() < Q.array(); // R < Q
R.array() <= Q.array(); // R <= Q
R.cwiseInverse(); // 1 ./ P
R.array().inverse(); // 1 ./ P
R.array().sin() // sin(P)
R.array().cos() // cos(P)
R.array().pow(s) // P .^ s
R.array().square() // P .^ 2
R.array().cube() // P .^ 3
R.cwiseSqrt() // sqrt(P)
R.array().sqrt() // sqrt(P)
R.array().exp() // exp(P)
R.array().log() // log(P)
R.cwiseMax(P) // max(R, P)
R.array().max(P.array()) // max(R, P)
R.cwiseMin(P) // min(R, P)
R.array().min(P.array()) // min(R, P)
R.cwiseAbs() // abs(P)
R.array().abs() // abs(P)
R.cwiseAbs2() // abs(P.^2)
R.array().abs2() // abs(P.^2)
(R.array() < s).select(P,Q); // (R < s ? P : Q)Eigen 矩阵化简
// Reductions.
int r, c;
// Eigen // Matlab
R.minCoeff() // min(R(:))
R.maxCoeff() // max(R(:))
s = R.minCoeff(&r, &c) // [s, i] = min(R(:)); [r, c] = ind2sub(size(R), i);
s = R.maxCoeff(&r, &c) // [s, i] = max(R(:)); [r, c] = ind2sub(size(R), i);
R.sum() // sum(R(:))
R.colwise().sum() // sum(R)
R.rowwise().sum() // sum(R, 2) or sum(R')'
R.prod() // prod(R(:))
R.colwise().prod() // prod(R)
R.rowwise().prod() // prod(R, 2) or prod(R')'
R.trace() // trace(R)
R.all() // all(R(:))
R.colwise().all() // all(R)
R.rowwise().all() // all(R, 2)
R.any() // any(R(:))
R.colwise().any() // any(R)
R.rowwise().any() // any(R, 2)Eigen 矩阵点乘
// Dot products, norms, etc.
// Eigen // Matlab
x.norm() // norm(x). Note that norm(R) doesn't work in Eigen.
x.squaredNorm() // dot(x, x) Note the equivalence is not true for complex
x.dot(y) // dot(x, y)
x.cross(y) // cross(x, y) Requires #include <Eigen/Geometry>Eigen 矩阵类型转换
//// Type conversion
// Eigen // Matlab
A.cast<double>(); // double(A)
A.cast<float>(); // single(A)
A.cast<int>(); // int32(A)
A.real(); // real(A)
A.imag(); // imag(A)
// if the original type equals destination type, no work is doneEigen 求解线性方程组 Ax = b
// Solve Ax = b. Result stored in x. Matlab: x = A \ b.
x = A.ldlt().solve(b)); // A sym. p.s.d. #include <Eigen/Cholesky>
x = A.llt() .solve(b)); // A sym. p.d. #include <Eigen/Cholesky>
x = A.lu() .solve(b)); // Stable and fast. #include <Eigen/LU>
x = A.qr() .solve(b)); // No pivoting. #include <Eigen/QR>
x = A.svd() .solve(b)); // Stable, slowest. #include <Eigen/SVD>
// .ldlt() -> .matrixL() and .matrixD()
// .llt() -> .matrixL()
// .lu() -> .matrixL() and .matrixU()
// .qr() -> .matrixQ() and .matrixR()
// .svd() -> .matrixU(), .singularValues(), and .matrixV()Eigen 矩阵特征值
// Eigenvalue problems
// Eigen // Matlab
A.eigenvalues(); // eig(A);
EigenSolver<Matrix3d> eig(A); // [vec val] = eig(A)
eig.eigenvalues(); // diag(val)
eig.eigenvectors(); // vec
// For self-adjoint matrices use SelfAdjointEigenSolver<>Cholesky分解法又叫平方根法，是求解对称正定线性方程组最常用的方法之一。对于一般矩阵，为了消除LU分解的局限性和误差的过分积累，采用了选主元的方法，但对于对称正定矩阵而言，选主元是不必要的。
参考博客：Eigen: C++开源矩阵计算工具——Eigen的简单用法
MatrixXd::Random(3,3)表示产生一个元素类型为double的3*3的临时矩阵对象。QR分解
Eigen的QR分解非常绕人，它总共提供了下面这些矩阵的分解方式：
| Decomposition | Method | Requirements on the matrix | Speed | Accuracy |
|---|---|---|---|---|
| PartialPivLU | partialPivLu() | Invertible | ++ | + |
| FullPivLU | fullPivLu() | None | - | +++ |
| HouseholderQR | householderQr() | None | ++ | + |
| ColPivHouseholderQR | colPivHouseholderQr() | None | + | ++ |
| FullPivHouseholderQR | fullPivHouseholderQr() | None | - | +++ |
| LLT | llt() | Positive definite | +++ | + |
| LDLT | ldlt() | Positive or negative semidefinite | +++ | ++ |
由于我只用到了QR分解，而且Eigen的QR分解开始使用时确实不容易入手，因此这里只提供了householderQR的分解方式的演示代码：
void QR2()
{
Matrix3d A;
A<<1,1,1,
2,-1,-1,
2,-4,5;
HouseholderQR<Matrix3d> qr;
qr.compute(A);
MatrixXd R = qr.matrixQR().triangularView<Upper>();
MatrixXd Q = qr.householderQ();
std::cout << "QR2(): HouseholderQR---------------------------------------------"<< std::endl;
std::cout << "A "<< std::endl <<A << std::endl << std::endl;
std::cout <<"qr.matrixQR()"<< std::endl << qr.matrixQR() << std::endl << std::endl;
std::cout << "R"<< std::endl <<R << std::endl << std::endl;
std::cout << "Q "<< std::endl <<Q << std::endl << std::endl;
std::cout <<"Q*R" << std::endl <<Q*R << std::endl << std::endl;
}
Example:
Matrix3f m = Matrix3f::Random();
Matrix3f y = Matrix3f::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the matrix y:" << endl << y << endl;
Matrix3f x;
x = m.colPivHouseholderQr().solve(y);
assert(y.isApprox(m*x));
cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;
Here is the matrix m:
0.68 0.597 -0.33
-0.211 0.823 0.536
0.566 -0.605 -0.444
Here is the matrix y:
0.108 -0.27 0.832
-0.0452 0.0268 0.271
0.258 0.904 0.435
Here is a solution x to the equation mx=y:
0.609 2.68 1.67
-0.231 -1.57 0.0713
0.51 3.51 1.05
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