What are some recent advances in non-convex optimization research?

What are some recent advances in non-convex optimization research? originally appeared on Quora – the knowledge sharing network where compelling questions are answered by people with unique insights. Answer by Anima Anandkumar, Faculty at UC Irvine, Machine learning researcher, on Quora. Non-convex optimization is now ubiquitous in machine learning. While previously, the focus was on convex relaxation methods, now the emphasis is on being able to solve non-convex problems directly. It is not possible to find the global optimum of every non-convex problem due to NP-hardness barrier. An alternate approach is: when can it be solved efficiently (preferably in low order polynomial time). Recent theoretical work has established that many non-convex problems can be solved near-optimally, but simple iterative algorithms, e.g. gradient descent with random restarts. The conditions for success…


Link to Full Article: What are some recent advances in non-convex optimization research?

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!