Convex majorization methods for nonconvex constrained problems achieve O(ε^{-(κ+1)/κ}) iteration complexity under Hölderian gradients, with a second-order variant reaching approximate second-order stationarity in O(1/ε1+1/ε2) subproblem solves.
Stochastic first-order methods for convex and nonconvex functional constrained optimization.Mathematical Programming, 197(1):215–279, 2023
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Complexity Analysis of Convex Majorization Schemes for Nonconvex Constrained Optimization
Convex majorization methods for nonconvex constrained problems achieve O(ε^{-(κ+1)/κ}) iteration complexity under Hölderian gradients, with a second-order variant reaching approximate second-order stationarity in O(1/ε1+1/ε2) subproblem solves.