@article {IOPORT.06030558,
    author       = {D\'esid\'eri, Jean-Antoine},
    title        = {Multiple-gradient descent algorithm (MGDA) for multiobjective optimization.},
    year         = {2012},
    journal      = {Comptes Rendus. Math\'ematique. Acad\'emie des Sciences, Paris},
    volume       = {350},
    number       = {5-6},
    issn         = {1631-073X},
    pages        = {313-318},
    publisher    = {Acad\'emie des Sciences, Paris; Elsevier, Paris},
    doi          = {10.1016/j.crma.2012.03.014},
    abstract     = {Summary: One considers the context of the concurrent optimization of several criteria $J_{i}(Y) (i=1,\ldots ,n)$, supposed to be smooth functions of the design vector $Y\in \Bbb R^{N} (n\leqslant N)$. An original constructive solution is given to the problem of identifying a descent direction common to all criteria when the current design-point $Y^{0}$ is not Pareto-optimal. This leads us to generalize the classical steepest-descent method to the multiobjective context by utilizing this direction for the descent. The algorithm is then proved to converge to a Pareto-stationary design-point.},
    identifier   = {06030558},
}