Similarities between Line search and Mathematical optimization
Line search and Mathematical optimization have 10 things in common (in Unionpedia): Conjugate gradient method, Gradient descent, Loss function, Maxima and minima, Nelder–Mead method, Newton's method in optimization, Pattern search (optimization), Quasi-Newton method, Simulated annealing, Trust region.
Conjugate gradient method
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive-definite.
Conjugate gradient method and Line search · Conjugate gradient method and Mathematical optimization ·
Gradient descent
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
Gradient descent and Line search · Gradient descent and Mathematical optimization ·
Loss function
In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
Line search and Loss function · Loss function and Mathematical optimization ·
Maxima and minima
In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given range (the local or relative extrema) or on the entire domain of a function (the global or absolute extrema).
Line search and Maxima and minima · Mathematical optimization and Maxima and minima ·
Nelder–Mead method
The Nelder–Mead method or downhill simplex method or amoeba method is a commonly applied numerical method used to find the minimum or maximum of an objective function in a multidimensional space.
Line search and Nelder–Mead method · Mathematical optimization and Nelder–Mead method ·
Newton's method in optimization
In calculus, Newton's method is an iterative method for finding the roots of a differentiable function (i.e. solutions to the equation). In optimization, Newton's method is applied to the derivative of a twice-differentiable function to find the roots of the derivative (solutions to), also known as the stationary points of.
Line search and Newton's method in optimization · Mathematical optimization and Newton's method in optimization ·
Pattern search (optimization)
Pattern search (also known as direct search, derivative-free search, or black-box search) is a family of numerical optimization methods that does not require a gradient.
Line search and Pattern search (optimization) · Mathematical optimization and Pattern search (optimization) ·
Quasi-Newton method
Quasi-Newton methods are methods used to either find zeroes or local maxima and minima of functions, as an alternative to Newton's method.
Line search and Quasi-Newton method · Mathematical optimization and Quasi-Newton method ·
Simulated annealing
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.
Line search and Simulated annealing · Mathematical optimization and Simulated annealing ·
Trust region
Trust region is a term used in mathematical optimization to denote the subset of the region of the objective function that is approximated using a model function (often a quadratic).
Line search and Trust region · Mathematical optimization and Trust region ·
The list above answers the following questions
- What Line search and Mathematical optimization have in common
- What are the similarities between Line search and Mathematical optimization
Line search and Mathematical optimization Comparison
Line search has 17 relations, while Mathematical optimization has 234. As they have in common 10, the Jaccard index is 3.98% = 10 / (17 + 234).
References
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