All functions |
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Perform axial search around a supposed minimum and provide diagnostics |
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Check bounds and masks for parameter constraints used in nonlinear optimization |
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Compute the maximum step along a search direction. |
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set control defaults |
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Run tests, where possible, on user objective function |
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Generate gradient and Hessian for a function at given parameters. |
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Generate gradient and Hessian for a function at given parameters. |
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Backward difference numerical gradient approximation. |
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Central difference numerical gradient approximation. |
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Run tests, where possible, on user objective function and (optionally) gradient and hessian |
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Forward difference numerical gradient approximation. |
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A reorganization of the call to numDeriv grad() function. |
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Run tests, where possible, on user objective function and (optionally) gradient and hessian |
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Check Kuhn Karush Tucker conditions for a supposed function minimum |
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Tools to Support Optimization Possibly with Bounds and Masks |
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Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization |
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