💡 Words with a Similar Meaning to "Minimax approximation algorithm"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| mm algorithm | an iterative optimization method which exploits the convexity of a function in order to find its maxima or minima. |
| minimax theorem | In the mathematical area of game theory and of convex optimization, a minimax theorem is a theorem that claims that |
| approximation theorynoun | (mathematics) The study of how functions can best be approximated with simpler functions, and how the errors introduced in this way can be quantitatively characterized. |
| maximum likelihood estimation | In statistics, maximum likelihood estimation is a method of estimating the parameters of an assumed probability distribution, given some observed data. |
| minimum mean square error | In statistics and signal processing, a minimum mean square error estimator is an estimation method which minimizes the mean square error, which is a common measure of estimator quality, of the fitted values of a dependent variable. |
| ellipsoid method | In mathematical optimization, the ellipsoid method is an iterative method for minimizing convex functions over convex sets. |
| linear programmingnoun | (mathematics) The branch of mathematics concerned with the minimization or maximization of a linear function of several variables and inequalities; used in many branches of industry to minimize costs or maximize production. |
| gradient descentnoun | (mathematics) A first-order iterative optimization algorithm for finding a local minimum of a differentiable function. |
| mirror descent | In mathematics, mirror descent is an iterative optimization algorithm for finding a local minimum of a differentiable function. |
| least absolute deviations | Least absolute deviations, also known as least absolute errors, least absolute residuals, or least absolute values, is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L1 norm of such values. |
| global optimization | a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. |
| empirical risk minimization | In statistical learning theory, the principle of empirical risk minimization defines a family of learning algorithms based on evaluating performance over a known and fixed dataset. |
| lagrange multiplier | In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). |
| minimax estimator | In statistical decision theory, where we are faced with the problem of estimating a deterministic parameter (vector) from observations an estimator (estimation rule) is called minimax if its maximal risk is minimal among all estimators of . |
| odds algorithm | In decision theory, the odds algorithm (or Bruss algorithm) is a mathematical method for computing optimal strategies for a class of problems that belong to the domain of optimal stopping problems. |
| ternary search | A ternary search algorithm is a technique in computer science for finding the minimum or maximum of a unimodal function. |
| maximum theorem | The maximum theorem provides conditions for the continuity of an optimized function and the set of its maximizers with respect to its parameters. |
| local optimum | — |
| logsumexpnoun | (mathematics) A smooth maximum used by machine learning algorithms. |
| bayes estimator | In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). |
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