The article shows how a stochastic estimator called SEMA can help solve difficult optimization problems without needing a lot of information. The researchers looked at different methods for solving these problems and found new ways to make them work better. They showed that certain methods like Adam can converge more easily with a higher momentum parameter. They also developed new methods for other types of optimization problems that work faster and more efficiently than before. Overall, their work improves on existing techniques and makes it easier to solve complex optimization challenges.