[handle_solve_pennon | e04svf | e04svc ]

Linear semidefinite programming can be viewed as a generalization of linear programming. While keeping many good properties of LP (such as the duality theory and solvability in polynomial time), SDP introduces a new highly nonlinear type of constraint – matrix inequality. It is an inequality on the eigenvalues of a matrix which depends on the decision variables. Typically, the matrix inequality is written in the form to request all eigenvalues of the matrix to be non-negative, thus the matrix is to be positive semidefinite

• Matrix completion using Semi-Definite Programming (SDP)
• Nearest correlation matrix using Semi-Definite Programming (SDP)
• Compute the Lovasz number of a graph using Semi-Definite Programming (SDP)

• Instructions on how to install the NAG Library for Python
• Instructions on how to run the Jupyter notebooks in the Repository