template < class xpr, class Out> void
padeapproximantofdegree(const xpr & a, const size_t & m, Out &f)
{
// padeapproximantofdegree pade approximant to exponential.
// f = padeapproximantofdegree(m) is the degree m diagonal
// pade approximant to exp(a), where m = 3, 5, 7, 9 or 13.
// series are evaluated in decreasing order of powers, which is
// in approx. increasing order of maximum norms of the terms.
typedef typename xpr::value_type value_type;
typedef typename meta::as_real<value_type>::type base_t;
typedef expm_helper<base_t> h_t;
typedef table <value_type> tab_t;
size_t n = nt2::length(a);
tab_t c = h_t::getpadecoefficients(m);
std::vector<tab_t> apowers(m/2+1);
// evaluate pade approximant.
tab_t u = nt2::zeros(n, n, meta::as_<value_type>());
tab_t v = u, u1;
switch (m)
{
case 3:
case 5:
case 7:
case 9:
apowers[0] = nt2::eye(n, meta::as_<value_type>());
apowers[1] = nt2::mtimes(a, a);
for (size_t j = 2; j < m/2+1; j++)
{
apowers[j] = nt2::mtimes(apowers[j-1], apowers[1]);
}
for(ptrdiff_t j=m+1; j >= 2 ; j-= 2)
{
// u = u+ c(j)*apowers[j/2-1];
u = nt2::fma(c(j),apowers[j/2-1], u);
}
u = mtimes(a, u);
for(ptrdiff_t j=m; j >= 1 ; j-= 2)
{
// v = v + c(j)*apowers[(j+1)/2-1];
v = nt2::fma(c(j),apowers[(j+1)/2-1], v);
}
break;
case 13:
// for optimal evaluation need different formula for m >= 12.
tab_t a2 = nt2::mtimes(a, a);
tab_t a4 = nt2::mtimes(a2, a2);
tab_t a6 = nt2::mtimes(a2, a4);
u = mtimes(a, (mtimes(a6,(c(14)*a6 + c(12)*a4 + c(10)*a2))+
c(8)*a6 + c(6)*a4 + nt2::fma(c(4), a2, c(2))));
v = mtimes(a6,c(13)*a6 + c(11)*a4 + c(9)*a2)
+ c(7)*a6 + c(5)*a4 + nt2::fma(c(3), a2, c(1));
}
f = nt2::linsolve((-u+v), (u+v));
}
int main(void) {
/*Matrix& A0 = *new DenseMatrix(new double[4] {1, 2, 3, 4}, 4, 1);
disp(A0);
Matrix& A1 = reshape(A0, new int[2] {2, 2});
disp(A1);*/
int m = 8;
int r = m / 4;
Matrix& L = randn(m, r);
Matrix& R = randn(m, r);
Matrix& A_star = mtimes(L, R.transpose());
Matrix& E_star0 = zeros(size(A_star));
int* indices = randperm(m * m);
int nz = m * m / 20;
int* nz_indices = new int[nz];
for (int i = 0; i < nz; i++) {
nz_indices[i] = indices[i] - 1;
}
Matrix& E_vec = vec(E_star0);
Matrix& Temp = (minus(rand(nz, 1), 0.5).times(100));
// disp(Temp);
setSubMatrix(E_vec, nz_indices, nz, new int[1] {0}, 1, Temp);
// disp(E_vec);
Matrix& E_star = reshape(E_vec, size(E_star0));
// disp(E_star);
// Input
Matrix& D = A_star.plus(E_star);
double lambda = 1 * pow(m, -0.5);
RobustPCA& robustPCA = *new RobustPCA(lambda);
robustPCA.feedData(D);
tic();
robustPCA.run();
fprintf("Elapsed time: %.2f seconds.\n", toc());
// Output
Matrix& A_hat = robustPCA.GetLowRankEstimation();
Matrix& E_hat = robustPCA.GetErrorMatrix();
fprintf("A*:\n");
disp(A_star, 4);
fprintf("A^:\n");
disp(A_hat, 4);
fprintf("E*:\n");
disp(E_star, 4);
fprintf("E^:\n");
disp(E_hat, 4);
fprintf("rank(A*): %d\n", rank(A_star));
fprintf("rank(A^): %d\n", rank(A_hat));
fprintf("||A* - A^||_F: %.4f\n", norm(A_star.minus(A_hat), "fro"));
fprintf("||E* - E^||_F: %.4f\n", norm(E_star.minus(E_hat), "fro"));
return EXIT_SUCCESS;
}
void QpToNlp::init(const Dict& opts) {
// Initialize the base classes
Qpsol::init(opts);
// Default options
string nlpsol_plugin;
Dict nlpsol_options;
// Read user options
for (auto&& op : opts) {
if (op.first=="nlpsol") {
nlpsol_plugin = op.second.to_string();
} else if (op.first=="nlpsol_options") {
nlpsol_options = op.second;
}
}
// Create a symbolic matrix for the decision variables
SX X = SX::sym("X", n_, 1);
// Parameters to the problem
SX H = SX::sym("H", sparsity_in(QPSOL_H));
SX G = SX::sym("G", sparsity_in(QPSOL_G));
SX A = SX::sym("A", sparsity_in(QPSOL_A));
// Put parameters in a vector
std::vector<SX> par;
par.push_back(H.nonzeros());
par.push_back(G.nonzeros());
par.push_back(A.nonzeros());
// The nlp looks exactly like a mathematical description of the NLP
SXDict nlp = {{"x", X}, {"p", vertcat(par)},
{"f", mtimes(G.T(), X) + 0.5*mtimes(mtimes(X.T(), H), X)},
{"g", mtimes(A, X)}};
// Create an Nlpsol instance
casadi_assert_message(!nlpsol_plugin.empty(), "'nlpsol' option has not been set");
solver_ = nlpsol("nlpsol", nlpsol_plugin, nlp, nlpsol_options);
alloc(solver_);
// Allocate storage for NLP solver parameters
alloc_w(solver_.nnz_in(NLPSOL_P), true);
}
NT2_TEST_CASE_TPL(tr_result, NT2_REAL_TYPES)
{
typedef nt2::table<T> t_t;
t_t a = nt2::tril( nt2::ones (4, 4, nt2::meta::as_<T>())
+ T(10)*nt2::eye (4, 4, nt2::meta::as_<T>()));
t_t b = nt2::ones(4, 1, nt2::meta::as_<T>());
nt2::display("a ", a);
nt2::display("b ", b);
nt2::details::tr_solve_result<t_t> f(a, b, 'L', 'N', 'N');
nt2::display("x", f.x());
NT2_DISPLAY(a);
NT2_TEST(nt2::isulpequal(b, mtimes(a, f.x()), T(2.0)));
}
/**
* Generate random samples chosen from the multivariate Gaussian
* distribution with mean MU and covariance SIGMA.
*
* X ~ N(u, Lambda) => Y = B * X + v ~ N(B * u + v, B * Lambda * B')
* Therefore, if X ~ N(0, Lambda),
* then Y = B * X + MU ~ N(MU, B * Lambda * B').
* We only need to do the eigen decomposition: SIGMA = B * Lambda * B'.
*
* @param MU 1 x d mean vector
*
* @param SIGMA covariance matrix
*
* @param cases number of d dimensional random samples
*
* @return cases-by-d sample matrix subject to the multivariate
* Gaussian distribution N(MU, SIGMA)
*
*/
Matrix& mvnrnd(Matrix& MU, Matrix& SIGMA, int cases) {
int d = MU.getColumnDimension();
if (MU.getRowDimension() != 1) {
errf("MU is expected to be 1 x %d matrix!\n", d);
}
if (norm(SIGMA.transpose().minus(SIGMA)) > 1e-10)
errf("SIGMA should be a %d x %d real symmetric matrix!\n", d);
Matrix** eigenDecompostion = eigs(SIGMA, d, "lm");
Matrix& B = *eigenDecompostion[0];
Matrix& Lambda = *eigenDecompostion[1];
/*disp(B);
disp(Lambda);*/
Matrix& X = *new DenseMatrix(d, cases);
std::default_random_engine generator(time(NULL));
std::normal_distribution<double> normal(0.0, 1.0);
double sigma = 0;
for (int i = 0; i < d; i++) {
sigma = Lambda.getEntry(i, i);
if (sigma == 0) {
X.setRowMatrix(i, zeros(1, cases));
continue;
}
if (sigma < 0) {
errf("Covariance matrix should be positive semi-definite!\n");
exit(1);
}
for (int n = 0; n < cases; n++) {
X.setEntry(i, n, normal(generator) * pow(sigma, 0.5));
}
}
Matrix& Y = plus(mtimes(B, X), repmat(MU.transpose(), 1, cases)).transpose();
return Y;
}
static void chsp(A0& c, size_t n, size_t k)
{
typedef container::table<V> tab_t;
// k = 1 case obtained from k = 0 case with one bigger n.
--n;
BOOST_AUTO_TPL(x, nt2::colvect(nt2::cospi(nt2::linspace(Zero<V>(), nt2::One<V>(), n+1))));
tab_t d = nt2::ones(n+1,1,T());
d(1) = Two<V>();
d(n+1) = Two<V>();
BOOST_AUTO_TPL(c1, mtimes(d, nt2::rowvect(nt2::rec(d))));
BOOST_AUTO_TPL(c2, nt2::sx(nt2::tag::minus_(), x, nt2::rowvect(x)));
c = c1/c2;
// Now fix diagonal and signs.
c(1) = (nt2::Two<V>()*nt2::sqr(n)+nt2::One<V>())/nt2::Six<V>();
for (size_t i=2; i <= n+1; ++i)
{
if(nt2::is_even(i))
{
c(_, i) = -c(_, i);
c(i, _) = -c(i, _);
}
if(i <= n)
{
c(i,i) = -x(i)/(Two<V>()*(nt2::oneminus(nt2::sqr(x(i)))));
}
else
{
c(i, i) = -c(1, 1);
}
}
if (k == 1)
{
tab_t c1_ = c(nt2::_(2, n+1), nt2::_(2, n+1));
c = c1_; //ALIASING
}
}
tab_t pinv(base_t epsi = -1 )const
{
epsi = epsi < 0 ? nt2::eps(w_(1)) : epsi;
tab_t w1 = nt2::if_else( gt(w_, length(a_)*epsi), nt2::rec(w_), Zero<base_t>());
return mtimes(trans(vt_), mtimes(from_diag(w1), trans(u_)));
}
template<class XPR> result_type solve(const XPR & b,
base_t epsi = Mone<base_t>() )const{
epsi = epsi < 0 ? nt2::eps(w_(1)) : epsi;
tab_t w1 = nt2::if_else( gt(w_, length(a_)*epsi), nt2::rec(w_), Zero<base_t>());
return mtimes(trans(vt_), mtimes(from_diag(w1), mtimes(trans(u_), b)));
}
void run() const
{
for(int i = 0; i < 100; ++i)
a3 = mtimes(trans(a1), a2);
}
void operator()()
{
for(int i = 0; i < 100; ++i)
a3 = mtimes(a1, a2);
}
Function qpsol_nlp(const std::string& name, const std::string& solver,
const std::map<std::string, M>& qp, const Dict& opts) {
// We have: minimize f(x) = 1/2 * x' H x + c'x
// subject to lbx <= x <= ubx
// lbg <= g(x) = A x + b <= ubg
M x, p, f, g;
for (auto&& i : qp) {
if (i.first=="x") {
x = i.second;
} else if (i.first=="p") {
p = i.second;
} else if (i.first=="f") {
f = i.second;
} else if (i.first=="g") {
g = i.second;
} else {
casadi_error("No such field: " + i.first);
}
}
if (g.is_empty(true)) g = M(0, 1); // workaround
// Gradient of the objective: gf == Hx + g
M gf = M::gradient(f, x);
// Identify the linear term in the objective
M c = substitute(gf, x, M::zeros(x.sparsity()));
// Identify the quadratic term in the objective
M H = M::jacobian(gf, x, true);
// Identify the constant term in the constraints
M b = substitute(g, x, M::zeros(x.sparsity()));
// Identify the linear term in the constraints
M A = M::jacobian(g, x);
// Create a function for calculating the required matrices vectors
Function prob(name + "_qp", {x, p}, {H, c, A, b});
// Create the QP solver
Function conic_f = conic(name + "_qpsol", solver,
{{"h", H.sparsity()}, {"a", A.sparsity()}}, opts);
// Create an MXFunction with the right signature
vector<MX> ret_in(NLPSOL_NUM_IN);
ret_in[NLPSOL_X0] = MX::sym("x0", x.sparsity());
ret_in[NLPSOL_P] = MX::sym("p", p.sparsity());
ret_in[NLPSOL_LBX] = MX::sym("lbx", x.sparsity());
ret_in[NLPSOL_UBX] = MX::sym("ubx", x.sparsity());
ret_in[NLPSOL_LBG] = MX::sym("lbg", g.sparsity());
ret_in[NLPSOL_UBG] = MX::sym("ubg", g.sparsity());
ret_in[NLPSOL_LAM_X0] = MX::sym("lam_x0", x.sparsity());
ret_in[NLPSOL_LAM_G0] = MX::sym("lam_g0", g.sparsity());
vector<MX> ret_out(NLPSOL_NUM_OUT);
// Get expressions for the QP matrices and vectors
vector<MX> v(NL_NUM_IN);
v[NL_X] = ret_in[NLPSOL_X0];
v[NL_P] = ret_in[NLPSOL_P];
v = prob(v);
// Call the QP solver
vector<MX> w(CONIC_NUM_IN);
w[CONIC_H] = v.at(0);
w[CONIC_G] = v.at(1);
w[CONIC_A] = v.at(2);
w[CONIC_LBX] = ret_in[NLPSOL_LBX];
w[CONIC_UBX] = ret_in[NLPSOL_UBX];
w[CONIC_LBA] = ret_in[NLPSOL_LBG] - v.at(3);
w[CONIC_UBA] = ret_in[NLPSOL_UBG] - v.at(3);
w[CONIC_X0] = ret_in[NLPSOL_X0];
w[CONIC_LAM_X0] = ret_in[NLPSOL_LAM_X0];
w[CONIC_LAM_A0] = ret_in[NLPSOL_LAM_G0];
w = conic_f(w);
// Get expressions for the solution
ret_out[NLPSOL_X] = w[CONIC_X];
ret_out[NLPSOL_F] = w[CONIC_COST];
ret_out[NLPSOL_G] = mtimes(v.at(2), w[CONIC_X]) + v.at(3);
ret_out[NLPSOL_LAM_X] = w[CONIC_LAM_X];
ret_out[NLPSOL_LAM_G] = w[CONIC_LAM_A];
ret_out[NLPSOL_LAM_P] = MX::nan(p.sparsity());
return Function(name, ret_in, ret_out, nlpsol_in(), nlpsol_out(),
{{"default_in", nlpsol_default_in()}});
}
Matrix& LASSO::train(Matrix& X, Matrix& Y, Options& options) {
int p = size(X, 2);
int ny = size(Y, 2);
double epsilon = options.epsilon;
int maxIter = options.maxIter;
double lambda = options.lambda;
bool calc_OV = options.calc_OV;
bool verbose = options.verbose;
/*XNX = [X, -X];
H_G = XNX' * XNX;
D = repmat(diag(H_G), [1, n_y]);
XNXTY = XNX' * Y;
A = (X' * X + lambda * eye(p)) \ (X' * Y);*/
Matrix& XNX = horzcat(2, &X, &uminus(X));
Matrix& H_G = XNX.transpose().mtimes(XNX);
double* Q = new double[size(H_G, 1)];
for (int i = 0; i < size(H_G, 1); i++) {
Q[i] = H_G.getEntry(i, i);
}
Matrix& XNXTY = XNX.transpose().mtimes(Y);
Matrix& A = mldivide(
plus(X.transpose().mtimes(X), times(lambda, eye(p))),
X.transpose().mtimes(Y)
);
/*AA = [subplus(A); subplus(-A)];
C = -XNXTY + lambda;
Grad = C + H_G * AA;
tol = epsilon * norm(Grad);
PGrad = zeros(size(Grad));*/
Matrix& AA = vertcat(2, &subplus(A), &subplus(uminus(A)));
Matrix& C = plus(uminus(XNXTY), lambda);
Matrix& Grad = plus(C, mtimes(H_G, AA));
double tol = epsilon * norm(Grad);
Matrix& PGrad = zeros(size(Grad));
std::list<double> J;
double fval = 0;
// J(1) = sum(sum((Y - X * A).^2)) / 2 + lambda * sum(sum(abs(A)));
if (calc_OV) {
fval = sum(sum(pow(minus(Y, mtimes(X, A)), 2))) / 2 +
lambda * sum(sum(abs(A)));
J.push_back(fval);
}
Matrix& I_k = Grad.copy();
double d = 0;
int k = 0;
DenseVector& SFPlusCi = *new DenseVector(AA.getColumnDimension());
Matrix& S = H_G;
Vector** SRows = null;
if (typeid(H_G) == typeid(DenseMatrix))
SRows = denseMatrix2DenseRowVectors(S);
else
SRows = sparseMatrix2SparseRowVectors(S);
Vector** CRows = null;
if (typeid(C) == typeid(DenseMatrix))
CRows = denseMatrix2DenseRowVectors(C);
else
CRows = sparseMatrix2SparseRowVectors(C);
double** FData = ((DenseMatrix&) AA).getData();
double* FRow = null;
double* pr = null;
int K = 2 * p;
while (true) {
/*I_k = Grad < 0 | AA > 0;
I_k_com = not(I_k);
PGrad(I_k) = Grad(I_k);
PGrad(I_k_com) = 0;*/
_or(I_k, lt(Grad, 0), gt(AA, 0));
Matrix& I_k_com = _not(I_k);
assign(PGrad, Grad);
logicalIndexingAssignment(PGrad, I_k_com, 0);
d = norm(PGrad, inf);
if (d < tol) {
if (verbose)
println("Converge successfully!");
break;
}
/*for i = 1:2*p
AA(i, :) = max(AA(i, :) - (C(i, :) + H_G(i, :) * AA) ./ (D(i, :)), 0);
end
A = AA(1:p,:) - AA(p+1:end,:);*/
for (int i = 0; i < K; i++) {
// SFPlusCi = SRows[i].operate(AA);
operate(SFPlusCi, *SRows[i], AA);
plusAssign(SFPlusCi, *CRows[i]);
timesAssign(SFPlusCi, 1 / Q[i]);
pr = SFPlusCi.getPr();
// F(i, :) = max(F(i, :) - (S(i, :) * F + C(i, :)) / D[i]), 0);
// F(i, :) = max(F(i, :) - SFPlusCi, 0)
FRow = FData[i];
for (int j = 0; j < AA.getColumnDimension(); j++) {
FRow[j] = max(FRow[j] - pr[j], 0);
}
}
// Grad = plus(C, mtimes(H_G, AA));
plus(Grad, C, mtimes(H_G, AA));
k = k + 1;
if (k > maxIter) {
if (verbose)
println("Maximal iterations");
break;
}
if (calc_OV) {
fval = sum(sum(pow(minus(Y, mtimes(XNX, AA)), 2))) / 2 +
lambda * sum(sum(abs(AA)));
J.push_back(fval);
}
if (k % 10 == 0 && verbose) {
if (calc_OV)
fprintf("Iter %d - ||PGrad||: %f, ofv: %f\n", k, d, J.back());
else
fprintf("Iter %d - ||PGrad||: %f\n", k, d);
}
}
Matrix& res = minus(
AA.getSubMatrix(0, p - 1, 0, ny - 1),
AA.getSubMatrix(p, 2 * p - 1, 0, ny - 1)
);
return res;
}