Quasi Convex Optimization

Quasi-convex optimization focuses on finding the minimum of a function that, while not necessarily convex, satisfies a weaker condition ensuring a unique minimum along any line segment. Current research emphasizes developing efficient algorithms, including adaptive multi-gradient methods and normalized gradient descent, to tackle non-convex problems arising in diverse applications like machine learning and time series forecasting. These advancements are particularly relevant for large-scale problems, improving the scalability and accuracy of solutions in areas such as neural network training and fairness certification. The resulting improvements in optimization efficiency have significant implications for various fields, enabling more robust and efficient solutions to complex real-world problems.