An algorithm for piecewise linear approximation of implicitly defined two-dimensional surfaces
SIAM Journal on Numerical Analysis
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Global optimization requires global information
Journal of Optimization Theory and Applications
Piecewise linear methods for nonlinear equations and optimization
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Generalized homotopy approach to multiobjective optimization
Journal of Optimization Theory and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Scalarization and stability in vector optimization
Journal of Optimization Theory and Applications
Piecewise linear approximation of smooth compact fibers
Journal of Complexity
Global Optimization: Fractal Approach and Non-redundant Parallelism
Journal of Global Optimization
Comparative Assessment of Algorithms and Software for Global Optimization
Journal of Global Optimization
Global Search Based on Efficient Diagonal Partitions and a Set of Lipschitz Constants
SIAM Journal on Optimization
Nonlinear optimization with GAMS /LGO
Journal of Global Optimization
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
IEEE Transactions on Evolutionary Computation
Delaunay refinement algorithms for triangular mesh generation
Computational Geometry: Theory and Applications
Hi-index | 0.00 |
Extending the notion of global search to multiobjective optimization is far than straightforward, mainly for the reason that one almost always has to deal with infinite Pareto optima and correspondingly infinite optimal values. Adopting Stephen Smale's global analysis framework, we highlight the geometrical features of the set of Pareto optima and we are led to consistent notions of global convergence. We formulate then a multiobjective version of a celebrated result by Stephens and Baritompa, about the necessity of generating everywhere dense sample sequences, and describe a globally convergent algorithm in case the Lipschitz constant of the determinant of the Jacobian is known.