tf.raw_ops.ResourceSparseApplyProximalGradientDescent

Sparse update '*var' as FOBOS algorithm with fixed learning rate.

Compat aliases for migration

tf.raw_ops.ResourceSparseApplyProximalGradientDescent(
    var, alpha, l1, l2, grad, indices, use_locking=False, name=None
)

That is for rows we have grad for, we update var as follows: prox_v = var - alpha * grad var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}

Args
`var`	A `Tensor` of type `resource`. Should be from a Variable().
`alpha`	A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar.
`l1`	A `Tensor`. Must have the same type as `alpha`. L1 regularization. Must be a scalar.
`l2`	A `Tensor`. Must have the same type as `alpha`. L2 regularization. Must be a scalar.
`grad`	A `Tensor`. Must have the same type as `alpha`. The gradient.
`indices`	A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum.
`use_locking`	An optional `bool`. Defaults to `False`. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
`name`	A name for the operation (optional).

Returns
The created Operation.

Args