non-convex optimization using gradient descent
Non-convex optimization using gradient descent refers to the process of finding the optimal solution for a problem with a non-convex objective function by iteratively adjusting the parameters in a direction given by the negative gradient of the function until convergence. It is applicable to a wide range of complex optimization problems, but due to the non-convex nature, it may get stuck in local optima rather than finding the global optimum.
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