A Projected Gradient Method for Vector Optimization Problems
Computational Optimization and Applications
A steepest descent method for vector optimization
Journal of Computational and Applied Mathematics
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
Newton's Method for Multiobjective Optimization
SIAM Journal on Optimization
Pareto set and EMOA behavior for simple multimodal multiobjective functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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In this paper we present a quasi-Newton's method for unconstrained multiobjective optimization of strongly convex objective functions. Hence, we can approximate the Hessian matrices by using the well known BFGS method. The approximation of the Hessian matrices is usually faster than their exact evaluation, as used in, e.g., recently proposed Newton's method for multiobjective optimization. We propose and analyze a new algorithm and prove that its convergence is superlinear.