Machine learning, Optimization Methods
in machine learning it is common to combine two optimization methods to get better results. this can be done using one method to optimize certain aspect of the model, and another method to otimize a different aspect . for example Stochastic gradient descent can be combined with momentum optimization to improve both convergence speed and accuracy.
THIS can be achieved by:
There are a few different ways to combine two optimization methods. One way is to use a hybrid algorithm. A hybrid algorithm is an algorithm that combines two or more different optimization methods. For example, a hybrid algorithm could combine a genetic algorithm with a gradient descent algorithm.
Another way to combine two optimization methods is to use a metaheuristic. A metaheuristic is a general-purpose optimization algorithm that can be used to solve a wide variety of optimization problems. For example, a metaheuristic could be used to solve a scheduling problem or an inventory problem.