A two-stage feasible directions algorithm for nonlinear constrained optimization
Mathematical Programming: Series A and B
SIAM Journal on Control and Optimization
SIAM Review
Matrix computations (3rd ed.)
Feasible direction interior-point technique for nonlinear optimization
Journal of Optimization Theory and Applications
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
Robust Truss Topology Design via Semidefinite Programming
SIAM Journal on Optimization
SIAM Journal on Optimization
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Minimax optimization problem of structural design
Computers and Structures
A feasible SQP-GS algorithm for nonconvex, nonsmooth constrained optimization
Numerical Algorithms
Hi-index | 0.00 |
We propose a new technique for minimization of convex functions not necessarily smooth. Our approach employs an equivalent constrained optimization problem and approximated linear programs obtained with cutting planes. At each iteration a search direction and a step length are computed. If the step length is considered "non serious", a cutting plane is added and a new search direction is computed. This procedure is repeated until a "serious" step is obtained. When this happens, the search direction is a feasible descent direction of the constrained equivalent problem. To compute the search directions we employ the same formulation as in FDIPA, the Feasible Directions Interior Point Algorithm for constrained optimization. We prove global convergence of the present method. A set of numerical tests is described. The present technique was also successfully applied to the topology optimization of robust trusses. Our results are comparable to those obtained with other well known established methods.