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Base class for optimization and system-solving algorithms. |
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Gradient based algorithms. |
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Algorithms that optimize an objective based on gradient and/or Hessian information. |
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Newton Updating of H . |
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Berndt Hall Hall Hausman Updating. |
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Container for algorithms that use but do not compute the Hessian matrix H. |
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Broyden Fletcher Goldfarb Shanno Updating of the hessian H. |
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[not coded yet] Davidon Fletcher Powell Updating of H. |
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Solve system of equations using Jacobian information. |
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Broyden approximation to the Jacobian. |
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Update the Jacobian on each iteration. |
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Sequential Quadratic Programming for constrained optimization. |
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Container for algorithms that do not rely on gradients. |
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Methods specific to solving or optimizing in one dimension. |
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One-dimensional line search for a maximum. |
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Constrained line maximization. |
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Systems line maximization. |
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Solve for the root of a OneDimSystem system using Bracket-Bisect. |
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The Nelder and Mead Amoeba (Simplex) algorithm. |
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Metropolis Simulated Annealing algorithm . |
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A special case of annealing in which the temperature stays the same and only improvements are accepted. |
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Client for parallel evaluation of objectives. |
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Base class for objective optimization and system solving. |
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Container for Constrained Objectives. |
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A non-linear system of equations to solve. |
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A nonlinear system for computing equilibrium. |
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A One Dimensional Non-linear system (can be solved with OneDimSolve). |
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Container for Unconstrained Objectives. |
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Represents a blacbox objective. |
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Constant elasticity of subsitution function. |
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Cobb-Douglas objective. |
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Base class for automatically generated econometric objectives: likelihood functions and GMM objective. |
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Objective for multinomial choice models. |
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Multinomial Logit Model. |
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Container for normally-distributed multinomial discrete choice reduced-form models. |
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Correlated MNP using GHK . |
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Independent MNP using Gausss-Hermite integration. |
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Represent sum of K BlackBox objectives. |
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Server for parallel evaluation of BlackBox objectives. |
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Container for discrete variables (DDP) and continuous parameters (FiveO). |
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Discrete values: actions, states, auxiliaries. |
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Continuously varying quantity: the base class for parameters of an Objective. |
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Value determined exactly by some other value, not chosen by optimization. |
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Can take on any real number: \(-\infty \lt v \lt \infty\). |
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Container for parameters are neither Free nor Determined. |
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A parameter contained in an open interval \(L \lt v \lt U\). |
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Bounded as: -1 < v < 1 . |
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Bounded as: 0 < v < 1 . |
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Parameter bounded from above \(-\infty \lt v \lt U\). |
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Bounded from above by 0. |
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A parameter bounded from below: \(L \lt v \lt \infty\). |
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Parameter Bounded from below by 0. |
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Two or more parameters whose ranges interact or are related for some other reason. |
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Vector of free parameters. |
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Vector of J probabilities that sum to strictly less than 1. |
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Vector of values determined exactly by some other value. |
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Vector of parameters that are sequentially decreasing. |
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Vector of parameters that are sequentially increasing. |
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Vector of Probabilities. |
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Vector of J probabilities that sum to 1. |
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Positive Vector. |
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A container for auxiliary structures, which helps organize the hierarchy of classes. |
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A continuous discretization of a (potentially) continuous quantity. |
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Code a system of constraints. |
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A container for equality constraints. |
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A container for inequality constraints. |
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Container for different integration techniques. |
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Gauss-Hermite Quadrature Integration. |
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Gauss-Laguerre Quadrature Integration. |
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Smooth Simulation of Multinomial Normal Probabilities and EValues. |
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Holds one line maximization try. |
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Store information about a multidimensional point. |
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Store a point for a constrained objective. |
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Store a point for a mixture objective. |
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Store a point for a separable objective. |
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A system point. |
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Checks minimum Ox version and prints copyright info. |
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