parameters: (
      learning_rate: float = 0.001,
      epscut: float = 1.0e-7,
      initial_accumulator_value: float = 0.1,
  )
return_statement: return adagrad(
          learning_rate, eps=epscut, initial_accumulator_value=initial_accumulator_value
      )
docstring: r"""AdaGrad Optimizer.
          In many cases, in Sgd the learning rate :math`\eta` should
          decay as a function of training iteration to prevent overshooting
          as the optimum is approached. AdaGrad is an adaptive learning
          rate algorithm that automatically scales the learning rate with a sum
          over past gradients. The vector :math:`\mathbf{g}` is initialized to zero.
          Given a stochastic estimate of the gradient of the cost function :math:`G(\mathbf{p})`,
          the updates for :math:`g_k` and the parameter :math:`p_k` are
  
  
          .. math:: g^\prime_k &= g_k + G_k(\mathbf{p})^2\\
                    p^\prime_k &= p_k - \frac{\eta}{\sqrt{g_k + \epsilon}}G_k(\mathbf{p})
  
          AdaGrad has been shown to perform particularly well when
          the gradients are sparse, but the learning rate may become too small
          after many updates because the sum over the squares of past gradients is cumulative.
  
  
          Args:
             learning_rate: Learning rate :math:`\eta`.
             epscut: Small :math:`\epsilon` cutoff.
             initial_accumulator_value: initial value of the accumulator
  
          Examples:
             Simple AdaGrad optimizer.
  
             >>> from netket.optimizer import AdaGrad
             >>> op = AdaGrad()
docstring_summary: r"""AdaGrad Optimizer.
          In many cases, in Sgd the learning rate :math`\eta` should
          decay as a function of training iteration to prevent overshooting
          as the optimum is approached. AdaGrad is an adaptive learning
          rate algorithm that automatically scales the learning rate with a sum
          over past gradients. The vector :math:`\mathbf{g}` is initialized to zero.
          Given a stochastic estimate of the gradient of the cost function :math:`G(\mathbf{p})`,
          the updates for :math:`g_k` and the parameter :math:`p_k` are
docstring_tokens: r AdaGrad Optimizer . In many cases in Sgd the learning rate : math \ eta should decay as a function of training iteration to prevent overshooting as the optimum is approached . AdaGrad is an adaptive learning rate algorithm that automatically scales the learning rate with a sum over past gradients . The vector : math : \ mathbf { g } is initialized to zero . Given a stochastic estimate of the gradient of the cost function : math : G ( \ mathbf { p } ) the updates for : math : g_k and the parameter : math : p_k are
function: def AdaGrad(
      learning_rate: float = 0.001,
      epscut: float = 1.0e-7,
      initial_accumulator_value: float = 0.1,
  ):
      r"""AdaGrad Optimizer.
          In many cases, in Sgd the learning rate :math`\eta` should
          decay as a function of training iteration to prevent overshooting
          as the optimum is approached. AdaGrad is an adaptive learning
          rate algorithm that automatically scales the learning rate with a sum
          over past gradients. The vector :math:`\mathbf{g}` is initialized to zero.
          Given a stochastic estimate of the gradient of the cost function :math:`G(\mathbf{p})`,
          the updates for :math:`g_k` and the parameter :math:`p_k` are
  
  
          .. math:: g^\prime_k &= g_k + G_k(\mathbf{p})^2\\
                    p^\prime_k &= p_k - \frac{\eta}{\sqrt{g_k + \epsilon}}G_k(\mathbf{p})
  
          AdaGrad has been shown to perform particularly well when
          the gradients are sparse, but the learning rate may become too small
          after many updates because the sum over the squares of past gradients is cumulative.
  
  
          Args:
             learning_rate: Learning rate :math:`\eta`.
             epscut: Small :math:`\epsilon` cutoff.
             initial_accumulator_value: initial value of the accumulator
  
          Examples:
             Simple AdaGrad optimizer.
  
             >>> from netket.optimizer import AdaGrad
             >>> op = AdaGrad()
          """
      from optax import adagrad
  
      return adagrad(
          learning_rate, eps=epscut, initial_accumulator_value=initial_accumulator_value
      )
function_tokens: def AdaGrad ( learning_rate : float = 0.001 , epscut : float = 1.0e-7 , initial_accumulator_value : float = 0.1 , ) : from optax import adagrad return adagrad ( learning_rate , eps = epscut , initial_accumulator_value = initial_accumulator_value )