add_concave_constraints(m[, tol])
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If the objective form of the parameter calculation is used, the data and the spline don't need to match exactly, and we can add constraints on the second derivatives that they are always negative. |
add_convex_constraints(m[, tol])
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If the objective form of the parameter calculation is used, the data and the spline don't need to match exactly, and we can add constraints on the second derivatives that they are always positive. |
add_decreasing_constraints(m[, tol])
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If the objective form of the parameter calculation is used, the data and the spline don't need to match exactly, and we can add constraints on the derivatives that they are negative at the knots. |
add_increasing_constraints(m[, tol])
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If the objective form of the parameter calculation is used, the data and the spline don't need to match exactly, and we can add constraints on the derivatives that they are positive at the knots. |
cubic_parameters_model(x_data, y_data[, ...])
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Create a Pyomo model to calculate parameters for a cubic spline. |