{ }
Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
tf.compat.v1.train.sdca_optimizer(
sparse_example_indices: Annotated[List[Any], _atypes.Int64],
sparse_feature_indices: Annotated[List[Any], _atypes.Int64],
sparse_feature_values: Annotated[List[Any], _atypes.Float32],
dense_features: Annotated[List[Any], _atypes.Float32],
example_weights: Annotated[Any, _atypes.Float32],
example_labels: Annotated[Any, _atypes.Float32],
sparse_indices: Annotated[List[Any], _atypes.Int64],
sparse_weights: Annotated[List[Any], _atypes.Float32],
dense_weights: Annotated[List[Any], _atypes.Float32],
example_state_data: Annotated[Any, _atypes.Float32],
loss_type: str,
l1: float,
l2: float,
num_loss_partitions: int,
num_inner_iterations: int,
adaptative: bool = True,
name=None
)
linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.
Proximal Stochastic Dual Coordinate Ascent.
Shai Shalev-Shwartz, Tong Zhang. 2012
\[Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|\]
Adding vs. Averaging in Distributed Primal-Dual Optimization.
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
Peter Richtarik, Martin Takac. 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities.
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015