Optimization Parameters
The optimization parameters define the optimization algorithm.
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pydantic model nopec.parameters.OptimizationParameters
Optimization parameters.
These parameters include the coefficients in the cost function \(\alpha\) and
\(\beta\), and the hidden parameters \(\mu^*\).
- Config:
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field alpha_cost: float = 100000.0
Cost function coefficient multiplying the observable discrepancy.
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field beta_cost: float = 1e-07
Cost function Coefficient multiplying the distance from the reference
parameter.
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field mu_1_a: float = 1.0
Left boundary of first control parameter.
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field mu_1_b: float = 5.0
Right boundary of first control parameter.
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field mu_2_a: float = 1.0
Left boundary of second control parameter.
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field mu_2_b: float = 5.0
Right boundary of second control parameter.
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field mu_3_a: float = 1.0
Left boundary of third control parameter.
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field mu_3_b: float = 5.0
Right boundary of third control parameter.
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field mu_4_a: float = 1.0
Left boundary of fourth control parameter.
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field mu_4_b: float = 5.0
Right boundary of fourth control parameter.
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field mu_ref: ndarray = array([3., 3., 3., 3.])
Initial reference parameter.
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field mu_star: ndarray = array([2., 3., 4., 5.])
Hidden parameter.