Convex Optimization for Machine Learning Model Tuning!

Hey everyone,

I am working on tuning hyperparameters for a machine learning model and wondering if convex optimization techniques can help streamline the process. Specifically…, I have a loss function that seems convex in some parameters but non-convex overall.

Are there any convex relaxation methods that can approximate a non-convex loss function effectively: ??
Would using projected gradient descent or other convex optimization techniques be beneficial in finding optimal hyperparameters: ??
Any recommended convex solvers or libraries that integrate well with ML frameworks like TensorFlow or PyTorch: ??

I would appreciate any insights or references to research papers that discuss convex optimization applications in ML hyperparameter tuning.

Thanks !!

With Regards,
Marcelo Mulesoft

You are unlikely to get an answer here. Since this forum is about the Cvx software.

I would try OR StackExchange instead.