Hi,
I understand that you need a faster and more efficient alrorithm to solve unconstrained quadratic problems.
For solving large-scale unconstrained quadratic programming problems where the matrix ( A ) is positive semidefinite, you can often achieve better performance by using specialized algorithms that take advantage of the problem's structure. Here are a few alternatives to MATLAB's quadprog that you might consider:
- Conjugate Gradient Method: This is an iterative method specifically designed for solving large systems of linear equations or large-scale unconstrained optimization problems, particularly when the matrix ( A ) is symmetric positive semidefinite. MATLAB's built-in 'pcg' (Preconditioned Conjugate Gradient) function can be used for this purpose. Refer to the following MathWorks documentation for detailed information about 'pcg' function: https://www.mathworks.com/help/matlab/ref/pcg.html
- LSQR: The LSQR algorithm is an iterative method for solving large-scale linear systems and least-squares problems. Although it's not specifically designed for quadratic programming, it can be effective for large and sparse systems. MATLAB's built-in 'lsqr' function can be used. Refer to the following MathWorks documentation for detailed information about 'lsqr' function: https://www.mathworks.com/help/matlab/ref/lsqr.html
I hope this resolves your issue.
