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In this paper, we present a new gradient method in the Riemannian context to solve multicriteria optimization. If the objective function is quasiconvex, the sequence generated by this method converges to a critical Pareto point. If the objective function is pseudo-convex, then the sequence will converge to optimal Pareto point.
In this note, we analyze a few major claims about . As a consequence, we
rewrite a major theorem, nullify its proof and one remark of importance, and
offer a valid proof for it. The most important gift of this paper is probably
the reasoning involved in all: We observe that a constant, namely t, has been changed into a variable, and
we then tell why such a move could not have been made, we observe the
discrepancy between the claimed domain and the actual domain of a supposed
function that is created and we then explain why such a function
could not, or should not, have been created, along with others.
We give a short and
easy proof of the characterization of differentiable quasiconvex functions.