Self-adaptive proximal algorithms for equilibrium problems in Hadamard space

Анотація

We consider a new self-adaptive algorithms for equilibrium problem in Hadamard spaces. At each step of the algorithms, the sequential minimization of two special strongly convex functions is performed. Our self-adaptive algorithms do not calculate bifunction values at additional points and do not require knowledge of bifunctions' Lipschitz constants. For pseudomonotone bifunctions of Lipschitz-type, theorems on weak convergence of sequences generated by algorithms are proved.