// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle

#include <Eigen/Core>
#include <Eigen/Array>

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.
double s;                            

// 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.
A.setRandom();    // Fill A with uniform random numbers in (-1, 1).
                  // Requires #include <Eigen/Array>.
A.setIdentity();  // Fill A with the identity.

// 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.start(n)                         // x(1:n)
x.start<n>()                       // x(1:n)
x.end(n)                           // N = rows(x); x(N - n: N)
x.end<n>()                         // N = rows(x); x(N - n: N)
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.corner(TopLeft, rows, cols)      // P(1:rows, 1:cols)
P.corner(TopRight, rows, cols)     // [m n]=size(P); P(1:rows, n-cols+1:n)
P.corner(BottomLeft, rows, cols)   // [m n]=size(P); P(m-rows+1:m, 1:cols)
P.corner(BottomRight, rows, cols)  // [m n]=size(P); P(m-rows+1:m, n-cols+1:n)
P.corner<rows,cols>(TopLeft)       // P(1:rows, 1:cols)
P.corner<rows,cols>(TopRight)      // [m n]=size(P); P(1:rows, n-cols+1:n)
P.corner<rows,cols>(BottomLeft)    // [m n]=size(P); P(m-rows+1:m, 1:cols)
P.corner<rows,cols>(BottomRight)   // [m n]=size(P); P(m-rows+1:m, n-cols+1:n)
P.minor(i, j)                      // Something nasty.

// 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])

// 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)

// 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;

 // Vectorized operations on each element independently
 // (most require #include <Eigen/Array>)
// Eigen                  // Matlab
R = P.cwise() * Q;        // R = P .* Q
R = P.cwise() / Q;        // R = P ./ Q
R = P.cwise() + s;        // R = P + s
R = P.cwise() - s;        // R = P - s
R.cwise() += s;           // R = R + s
R.cwise() -= s;           // R = R - s
R.cwise() *= s;           // R = R * s
R.cwise() /= s;           // R = R / s
R.cwise() < Q;            // R < Q
R.cwise() <= Q;           // R <= Q
R.cwise().inverse();      // 1 ./ P
R.cwise().sin()           // sin(P)
R.cwise().cos()           // cos(P)
R.cwise().pow(s)          // P .^ s
R.cwise().square()        // P .^ 2
R.cwise().cube()          // P .^ 3
R.cwise().sqrt()          // sqrt(P)
R.cwise().exp()           // exp(P)
R.cwise().log()           // log(P)
R.cwise().max(P)          // max(R, P)
R.cwise().min(P)          // min(R, P)
R.cwise().abs()           // abs(P)
R.cwise().abs2()          // abs(P.^2)
(R.cwise() < s).select(P,Q);  // (R < s ? P : Q)

// Reductions.
int r, c;
// Eigen                  // Matlab
R.minCoeff()              // min(R(:))
R.maxCoeff()              // max(R(:))
s = R.minCoeff(&r, &c)    // [aa, bb] = min(R); [cc, dd] = min(aa);
                          // r = bb(dd); c = dd; s = cc
s = R.maxCoeff(&r, &c)    // [aa, bb] = max(R); [cc, dd] = max(aa);
                          // row = bb(dd); col = dd; s = cc
R.sum()                   // sum(R(:))
R.colwise.sum()           // sum(R)
R.rowwise.sum()           // sum(R, 2) or sum(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)

// 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 can map existing memory into Eigen matrices.
float array[3];
Map<Vector3f>(array, 3).fill(10);
int data[4] = 1, 2, 3, 4;
Matrix2i mat2x2(data);
MatrixXi mat2x2 = Map<Matrix2i>(data);
MatrixXi mat2x2 = Map<MatrixXi>(data, 2, 2);

// Solve Ax = b. Result stored in x. Matlab: x = A \ b.
bool solved;
solved = A.ldlt().solve(b, &x));  // A sym. p.s.d.    #include <Eigen/Cholesky>
solved = A.llt() .solve(b, &x));  // A sym. p.d.      #include <Eigen/Cholesky>
solved = A.lu()  .solve(b, &x));  // Stable and fast. #include <Eigen/LU>
solved = A.qr()  .solve(b, &x));  // No pivoting.     #include <Eigen/QR>
solved = A.svd() .solve(b, &x));  // Stable, slowest. #include <Eigen/SVD>
// .ldlt() -> .matrixL() and .matrixD()
// .llt()  -> .matrixL()
// .lu()   -> .matrixL() and .matrixU()
// .qr()   -> .matrixQ() and .matrixR()
// .svd()  -> .matrixU(), .singularValues(), and .matrixV()

// Eigenvalue problems
// Eigen                          // Matlab
A.eigenvalues();                  // eig(A);
EigenSolver<Matrix3d> eig(A);     // [vec val] = eig(A)
eig.eigenvalues();                // diag(val)
eig.eigenvectors();               // vec