optimal_control_gradient_descent: estimating the optimal control using the dynamic elastic net
In seeds: Estimate Hidden Inputs using the Dynamic Elastic Net

estimating the optimal control using the dynamic elastic net

optimal_control_gradient_descent(
  alphaStep,
  armijoBeta,
  x0,
  parameters,
  alpha1,
  alpha2,
  measData,
  constStr,
  SD,
  modelFunc,
  measFunc,
  modelInput,
  optW,
  origAUC,
  maxIteration,
  plotEsti,
  conjGrad,
  eps,
  nnStates,
  verbose
)

`alphaStep`	starting value of the stepsize for the gradient descent, will be calculate to minimize the cost function by backtracking algorithm
`armijoBeta`	scaling of the alphaStep to find a approximately optimal value for the stepsize
`x0`	initial state of the ode system
`parameters`	parameters of the ODE-system
`alpha1`	L1 cost term scalar
`alpha2`	L2 cost term scalar
`measData`	measured values of the experiment
`constStr`	a string that represents constrains, can be used to calculate a hidden input for a component that gradient is zero
`SD`	standard deviation of the experiment; leave empty if unknown; matrix should contain the timesteps in the first column
`modelFunc`	function that describes the ODE-system of the model
`measFunc`	function that maps the states to the outputs
`modelInput`	an dataset that describes the external input of the system
`optW`	vector that indicated at which knots of the network the algorithm should estimate the hidden inputs
`origAUC`	AUCs of the first optimization; only used by the algorithm
`maxIteration`	a upper bound for the maximal number of iterations
`plotEsti`	boolean that controls of the current estimates should be plotted
`conjGrad`	boolean that indicates the usage of conjugate gradient method over the normal steepest descent
`eps`	citeria for stopping the algorithm
`nnStates`	a bit vector indicating the states that should be non negative
`verbose`	Boolean indicating if an output in the console should be created to display the gradient descent steps