A mesh-independence principle for operator equations and their discretizations
SIAM Journal on Numerical Analysis
A relaxed version of Bregman's method for convex programming
Journal of Optimization Theory and Applications
On the convergence of the proximal point algorithm for convex minimization
SIAM Journal on Control and Optimization
Inequalities in Banach spaces with applications
Nonlinear Analysis: Theory, Methods & Applications
Proximal minimization algorithm with D-functions
Journal of Optimization Theory and Applications
Mathematical Programming: Series A and B
Proximal Minimization Methods with Generalized Bregman Functions
SIAM Journal on Control and Optimization
Approximate iterations in Bregman-function-based proximal algorithms
Mathematical Programming: Series A and B
Newton's Mesh Independence Principle for a Class Of Optimal Shape Design Problems
SIAM Journal on Control and Optimization
A Proximal Point Method for the Variational Inequality Problem in Banach Spaces
SIAM Journal on Control and Optimization
A Generalized Proximal Point Algorithm for the Variational Inequality Problem in a Hilbert Space
SIAM Journal on Optimization
Strong Convergence of a Proximal-Type Algorithm in a Banach Space
SIAM Journal on Optimization
Local Convergence of the Proximal Point Algorithm and Multiplier Methods Without Monotonicity
Mathematics of Operations Research
A Projected Gradient Method for Vector Optimization Problems
Computational Optimization and Applications
Proximal Methods in Vector Optimization
SIAM Journal on Optimization
Calmness and Exact Penalization in Vector Optimization with Cone Constraints
Computational Optimization and Applications
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In this paper, we consider a convex vector optimization problem of finding weak Pareto optimal solutions for an extended vector-valued map from a uniformly convex and uniformly smooth Banach space to a real Banach space, with respect to the partial order induced by a closed, convex and pointed cone with a nonempty interior. We propose an inexact vector-valued proximal-type point algorithm based on a Lyapunov functional when the iterates are computed approximately and prove the sequence generated by the algorithm weakly converges to a weak Pareto optimal solution of the vector optimization problem under some mild conditions. Our results improve and generalize some known results.