SGDA-B is the first backtracking-enabled stochastic GDA algorithm for nonconvex-concave minimax problems that achieves the best known complexity bounds among methods agnostic to L, μ, and σ².
On the convergence properties of non-euclidean extragradient meth- ods for variational inequalities with generalized monotone operators
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A Stochastic GDA Method With Backtracking For Solving Nonconvex Concave Minimax Problems
SGDA-B is the first backtracking-enabled stochastic GDA algorithm for nonconvex-concave minimax problems that achieves the best known complexity bounds among methods agnostic to L, μ, and σ².