# Archive | Optimization ## One-Dimensional (1D) Test Functions for Function Optimization

Function optimization is a field of study that seeks an input to a function that results in the maximum or minimum output of the function. There are a large number of optimization algorithms and it is important to study and develop intuitions for optimization algorithms on simple and easy-to-visualize test functions. One-dimensional functions take a […] ## Line Search Optimization With Python

The line search is an optimization algorithm that can be used for objective functions with one or more variables. It provides a way to use a univariate optimization algorithm, like a bisection search on a multivariate objective function, by using the search to locate the optimal step size in each dimension from a known point […] ## Gradient Descent With RMSProp from Scratch

Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that it uses the same step size (learning rate) for each input variable. AdaGrad, for short, is an extension of the gradient descent optimization algorithm […] ## Dual Annealing Optimization With Python

Dual Annealing is a stochastic global optimization algorithm. It is an implementation of the generalized simulated annealing algorithm, an extension of simulated annealing. In addition, it is paired with a local search algorithm that is automatically performed at the end of the simulated annealing procedure. This combination of effective global and local search procedures provides […] ## A Gentle Introduction to the BFGS Optimization Algorithm

The Broyden, Fletcher, Goldfarb, and Shanno, or BFGS Algorithm, is a local search optimization algorithm. It is a type of second-order optimization algorithm, meaning that it makes use of the second-order derivative of an objective function and belongs to a class of algorithms referred to as Quasi-Newton methods that approximate the second derivative (called the […] ## How to Implement Gradient Descent Optimization from Scratch

Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. It is a simple and effective technique that can be implemented with just a few lines of code. It also provides the basis for many extensions and modifications that can result […] ## What Is a Gradient in Machine Learning?

Gradient is a commonly used term in optimization and machine learning. For example, deep learning neural networks are fit using stochastic gradient descent, and many standard optimization algorithms used to fit machine learning algorithms use gradient information. In order to understand what a gradient is, you need to understand what a derivative is from the […] Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that it uses the same step size (learning rate) for each input variable. AdaGradn and RMSProp are extensions to gradient descent that add a self-adaptive […] ## Iterated Local Search From Scratch in Python

Iterated Local Search is a stochastic global optimization algorithm. It involves the repeated application of a local search algorithm to modified versions of a good solution found previously. In this way, it is like a clever version of the stochastic hill climbing with random restarts algorithm. The intuition behind the algorithm is that random restarts […] ## Two-Dimensional (2D) Test Functions for Function Optimization

Function optimization is a field of study that seeks an input to a function that results in the maximum or minimum output of the function. There are a large number of optimization algorithms and it is important to study and develop intuitions for optimization algorithms on simple and easy-to-visualize test functions. Two-dimensional functions take two […]