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BLMVM

The NEOS Server offers BLMVM for the solution of nonlinear optimization problems with simple bounds on the variables.

This solver is a limited memory, variable metric method. It assumes that the variable bounds are fixed and the gradient of the objective function can be computed. This solver creates a quadratic model function in the full space of variables using projected gradients. This gradient information is stored in such a way that the storage required is linear in number of variables. Since the algorithm does not require second derivatives, the method can be applied when the Hessian is not available or not practical to compute.

If it is desired, BLMVM can solve the minimization problem without requiring the user to provide any gradient evaluations. This is accomplished through the automatic differentiation tools ADIC and ADIFOR.

To use BLMVM with AMPL files, try the BLMVM (AMPL input) page.

BLMVM was developed by Steve Benson and Jorge Moré. The algorithm can be found in the Toolkit for Advanced Optimization.

Using the NEOS Server for BLMVM

• The number of variables
• A subroutine that defines the starting point (optional)
• A subroutine that defines the bounds on the variables
• A subroutine that evaluates the objective function and gradient or a subroutine that evaluates only the objective function (using automatic differentiation).

Enter the location of the ampl model (local file)
Model File:

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