Adjoint-based, large-scale optimization of metamaterials

Towards Adjoint Based Constrained Optimization

Large-Scale Dynamic Optimization with the Directional Second Order Adjoint Method

Efficient solution of large-scale dynamic optimization problems can be achieved by exploiting the advantages of state-of-the-art integration and large-scale nonlinear programming tools. TruncatedNewton method provides an effective way to solve large-scale optimization problems by achieving savings in computation and storage. For dynamic optimization, the Hessian-vector products required by thes...

Large Scale Unconstrained Optimization

This paper reviews advances in Newton quasi Newton and conjugate gradi ent methods for large scale optimization It also describes several packages developed during the last ten years and illustrates their performance on some practical problems Much attention is given to the concept of partial separa bility which is gaining importance with the arrival of automatic di erentiation tools and of opt...

THE OPTIMIZATION OF LARGE-SCALE DOME TRUSSES ON THE BASIS OF THE PROBABILITY OF FAILURE

Metaheuristic algorithms are preferred by the many researchers to reach the reliability based design optimization (RBDO) of truss structures. The cross-sectional area of the elements of a truss is considered as design variables for the size optimization under frequency constraints. The design of dome truss structures are optimized based on reliability by a popular metaheuristic optimization tec...

Large-Scale Optimization of Eigenvalues

Optimization problems involving eigenvalues arise in many applications. Let x be a vector of real parameters and let A(x) be a continuously diierentiable symmetric matrix function of x. We consider a particular problem which occurs frequently: the minimization of the maximum eigenvalue of A(x), subject to linear constraints and bounds on x. The eigenvalues of A(x) are not diierentiable at point...