Optimization

Optimization Algorithms are responsible for solving Optimization Problem for a given function by finding the input parameters or arguments for that function that would result in the estimated minimum or maximum output of the Objective Function.

Optimization Algorithms improve learning efficiently of Machine Learning Models and help them converge to optimal solutions. It's often done by minimizing the cost function by updating model coefficients (for Regression) or Weights (for Artificial Neural Networks (ANN)).


Algorithms:


Note:

  • For functions to be optimizable, it must be a differentiable function(either univariate or multivariate).
  • Gradient-based optimization is the core of most optimization methods.
  • Among Gradient Descent Algorithms, Batch Gradient Descent is the most efficient, and Stochastic Gradient Descent(SGD) is more robust. Mini-batch Gradient Descent however is a good balance between the two, and therefor more commonly used.

Learning Material:

References: