State
Tracks the information about the optimizer, including the current point, its value, gradient, and then any history. Also includes information for checking convergence.
- Value parameters:
- adjustedGradient
f'(x) + r'(x), where r is any regularization added to the objective. For LBFGS, this is f'(x).
- adjustedValue
f(x) + r(x), where r is any regularization added to the objective. For LBFGS, this is f(x).
- convergenceReason
the convergence reason
- grad
f.gradientAt(x)
- history
any information needed by the optimizer to do updates.
- initialAdjVal
f(x_0) + r(x_0), used for checking convergence
- iter
what iteration number we are on.
- searchFailed
did the line search fail?
- value
f(x)
- x
the current point being considered