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- #ifndef IGL_MOSEK_QUADPROG_H
- #define IGL_MOSEK_QUADPROG_H
- #include "../igl_inline.h"
- #include <vector>
- #define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
- #include <Eigen/Dense>
- #include <Eigen/Sparse>
- namespace igl
- {
- struct MosekData
- {
- };
- // Solve a convex quadratic optimization problem with linear and constant
- // bounds, that is:
- //
- // Minimize: ½ * xT * Q⁰ * x + cT * x + cf
- //
- // Subject to: lc ≤ Ax ≤ uc
- // lx ≤ x ≤ ux
- //
- // where we are trying to find the optimal vector of values x.
- //
- // Note: Q⁰ must be symmetric and the ½ is a convention of MOSEK
- //
- // Note: Because of how MOSEK accepts different parts of the system, Q should
- // be stored in IJV (aka Coordinate) format and should only include entries in
- // the lower triangle. A should be stored in Column compressed (aka Harwell
- // Boeing) format. As described:
- // http://netlib.org/linalg/html_templates/node92.html
- // or
- // http://en.wikipedia.org/wiki/Sparse_matrix
- // #Compressed_sparse_column_.28CSC_or_CCS.29
- //
- //
- // Templates:
- // Index type for index variables
- // Scalar type for floating point variables (gets cast to double?)
- // Input:
- // n number of variables, i.e. size of x
- // Qi vector of qnnz row indices of non-zeros in LOWER TRIANGLE ONLY of Q⁰
- // Qj vector of qnnz column indices of non-zeros in LOWER TRIANGLE ONLY of
- // Q⁰
- // Qv vector of qnnz values of non-zeros in LOWER TRIANGLE ONLY of Q⁰,
- // such that:
- // Q⁰(Qi[k],Qj[k]) = Qv[k] for k ∈ [0,Qnnz-1], where Qnnz is the number of
- // non-zeros in Q⁰
- // c (optional) vector of n values of c, transpose of coefficient row vector
- // of linear terms, EMPTY means c == 0
- // cf (optional) value of constant term in objective, 0 means cf == 0, so
- // optional only in the sense that it is mandatory
- // m number of constraints, therefore also number of rows in linear
- // constraint coefficient matrix A, and in linear constraint bound vectors
- // lc and uc
- // Av vector of non-zero values of A, in column compressed order
- // Ari vector of row indices corresponding to non-zero values of A,
- // Acp vector of indices into Ari and Av of the first entry for each column
- // of A, size(Acp) = (# columns of A) + 1 = n + 1
- // lc vector of m linear constraint lower bounds
- // uc vector of m linear constraint upper bounds
- // lx vector of n constant lower bounds
- // ux vector of n constant upper bounds
- // Output:
- // x vector of size n to hold output of optimization
- // Return:
- // true only if optimization was successful with no errors
- //
- // Note: All indices are 0-based
- //
- template <typename Index, typename Scalar>
- IGL_INLINE bool mosek_quadprog(
- const Index n,
- /* mosek won't allow this to be const*/ std::vector<Index> & Qi,
- /* mosek won't allow this to be const*/ std::vector<Index> & Qj,
- /* mosek won't allow this to be const*/ std::vector<Scalar> & Qv,
- const std::vector<Scalar> & c,
- const Scalar cf,
- const Index m,
- /* mosek won't allow this to be const*/ std::vector<Scalar> & Ari,
- /* mosek won't allow this to be const*/ std::vector<Index> & Ari,
- const std::vector<Index> & Acp,
- const std::vector<Scalar> & lc,
- const std::vector<Scalar> & uc,
- const std::vector<Scalar> & lx,
- const std::vector<Scalar> & ux,
- MosekData & mosek_data,
- std::vector<Scalar> & x);
- // Wrapper with Eigen elements
- //// Templates:
- //// Scalar Scalar type for sparse matrix (e.g. double)
- //// Derived dervied type from matrix/vector (e.g. VectorXd)
- IGL_INLINE bool mosek_quadprog(
- const Eigen::SparseMatrix<double> & Q,
- const Eigen::VectorXd & c,
- const double cf,
- const Eigen::SparseMatrix<double> & A,
- const Eigen::VectorXd & lc,
- const Eigen::VectorXd & uc,
- const Eigen::VectorXd & lx,
- const Eigen::VectorXd & ux,
- MosekData & mosek_data,
- Eigen::VectorXd & x);
- }
- #ifdef IGL_HEADER_ONLY
- # include "mosek_quadprog.cpp"
- #endif
- #endif
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