Index _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z _ __add__() (decent_bench.costs.BaseRegularizerCost method) (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.PyTorchCost method) (decent_bench.costs.QuadraticCost method) (decent_bench.costs.ScaledCost method) (decent_bench.costs.SumCost method) (decent_bench.costs.ZeroCost method) __init__() (decent_bench.costs.BaseRegularizerCost method) (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.FractionalQuadraticRegularizerCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) (decent_bench.costs.QuadraticCost method) (decent_bench.costs.ScaledCost method) (decent_bench.costs.SumCost method) (decent_bench.costs.ZeroCost method) _get_batch_data() (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) _sample_batch_indices() (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.PyTorchCost method) A absolute() (in module decent_bench.utils.interoperability) AcceleratedGradientDescent (class in decent_bench.centralized_algorithms) Accuracy (class in decent_bench.metrics.metric_library) active_agents() (decent_bench.networks.FedNetwork method) (decent_bench.networks.Network method) active_clients() (decent_bench.networks.FedNetwork method) active_connected_agents() (decent_bench.networks.Network method) active_neighbors() (decent_bench.networks.P2PNetwork method) AdaptThenCombine (in module decent_bench.algorithms.p2p) add() (in module decent_bench.utils.interoperability) adjacency (decent_bench.networks.P2PNetwork property) ADMM (class in decent_bench.algorithms.p2p) ADMM_Tracking (in module decent_bench.algorithms.p2p) ADMM_TrackingGradient (in module decent_bench.algorithms.p2p) advance_progress_bar() (decent_bench.utils.progress_bar.ProgressBarHandle method) Agent (class in decent_bench.agents) AgentActivationScheme (class in decent_bench.schemes) AgentHistory (class in decent_bench.agents) AgentMetricsView (class in decent_bench.metrics) agents() (decent_bench.metrics.NetworkMetricsView method) (decent_bench.networks.FedNetwork method) (decent_bench.networks.Network method) aggregate() (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.federated.FedDyn method) (decent_bench.algorithms.federated.FedLT method) (decent_bench.algorithms.federated.FedNova method) (decent_bench.algorithms.federated.FedOpt method) (decent_bench.algorithms.federated.FedPD method) (decent_bench.algorithms.federated.Scaffold method) Algorithm (class in decent_bench.algorithms) algorithms (decent_bench.benchmark.MetricResult property) all_sorted_iterations() (in module decent_bench.metrics.utils) alpha (decent_bench.algorithms.p2p.LT_ADMM attribute) AlwaysActive (class in decent_bench.schemes) append_metadata() (decent_bench.utils.checkpoint_manager.CheckpointManager method) are_metrics_computed() (decent_bench.utils.checkpoint_manager.CheckpointManager method) argmax() (in module decent_bench.utils.interoperability) ArgmaxActivation (class in decent_bench.utils.pytorch_utils) argmin() (in module decent_bench.utils.interoperability) ArgminActivation (class in decent_bench.utils.pytorch_utils) Array (class in decent_bench.utils.array) ArrayKey (in module decent_bench.utils.types) ArrayLike (in module decent_bench.utils.types) astype() (in module decent_bench.utils.interoperability) ATC (class in decent_bench.algorithms.p2p) ATC_DIGing (in module decent_bench.algorithms.p2p) ATC_Tracking (class in decent_bench.algorithms.p2p) ATCT (in module decent_bench.algorithms.p2p) ATG (class in decent_bench.algorithms.p2p) AugDGM (class in decent_bench.algorithms.p2p) autodecorate_cost_method() (in module decent_bench.utils.interoperability) aux_step_size (decent_bench.algorithms.p2p.KGT attribute) (decent_bench.algorithms.p2p.LED attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) (decent_bench.algorithms.p2p.ProxSkip attribute) aux_vars (decent_bench.agents.Agent property) B BaseRegularizerCost (class in decent_bench.costs) batch_size (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.EmpiricalRiskCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) batch_used (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.EmpiricalRiskCost property) benchmark() (in module decent_bench.benchmark) BenchmarkProblem (class in decent_bench.benchmark) BenchmarkResult (class in decent_bench.benchmark) beta_1 (decent_bench.algorithms.federated.FedOpt attribute) beta_2 (decent_bench.algorithms.federated.FedAdam attribute) (decent_bench.algorithms.federated.FedYogi attribute) broadcast() (decent_bench.networks.FedNetwork method) (decent_bench.networks.P2PNetwork method) by_channel() (decent_bench.agents.ReceivedMessages method) C c0 (decent_bench.algorithms.federated.Scaffold attribute) checkpoint_size() (decent_bench.utils.checkpoint_manager.CheckpointManager method) CheckpointManager (class in decent_bench.utils.checkpoint_manager) chi (decent_bench.algorithms.p2p.ProxSkip attribute) choice() (in module decent_bench.utils.interoperability) cleanup() (decent_bench.algorithms.Algorithm method) (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.p2p.P2PAlgorithm method) cleanup_agents() (decent_bench.algorithms.Algorithm method) (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.p2p.P2PAlgorithm method) clear() (decent_bench.agents.ReceivedMessages method) (decent_bench.utils.checkpoint_manager.CheckpointManager method) ClientDriftFromServer (class in decent_bench.metrics.metric_library) clients() (decent_bench.metrics.NetworkMetricsView method) (decent_bench.networks.FedNetwork method) ClientSelectionScheme (class in decent_bench.schemes) comm_probability (decent_bench.algorithms.p2p.ProxSkip attribute) communication (decent_bench.metrics.ComputationalCost attribute) compress() (decent_bench.schemes.CompressionScheme method) (decent_bench.schemes.NoCompression method) (decent_bench.schemes.Quantization method) (decent_bench.schemes.RandK method) (decent_bench.schemes.StochasticQuantization method) (decent_bench.schemes.TopK method) compressed_msg_size() (decent_bench.schemes.CompressionScheme method) (decent_bench.schemes.RandK method) (decent_bench.schemes.TopK method) CompressionScheme (class in decent_bench.schemes) ComputationalCost (class in decent_bench.metrics) compute() (decent_bench.metrics.Metric method) (decent_bench.metrics.metric_library.Accuracy method) (decent_bench.metrics.metric_library.ClientDriftFromServer method) (decent_bench.metrics.metric_library.ConsensusError method) (decent_bench.metrics.metric_library.FractionSelectedClients method) (decent_bench.metrics.metric_library.FunctionCalls method) (decent_bench.metrics.metric_library.GradientCalls method) (decent_bench.metrics.metric_library.GradientNorm method) (decent_bench.metrics.metric_library.HessianCalls method) (decent_bench.metrics.metric_library.Loss method) (decent_bench.metrics.metric_library.MSE method) (decent_bench.metrics.metric_library.Precision method) (decent_bench.metrics.metric_library.ProximalCalls method) (decent_bench.metrics.metric_library.Recall method) (decent_bench.metrics.metric_library.ReceivedMessages method) (decent_bench.metrics.metric_library.Regret method) (decent_bench.metrics.metric_library.SentMessages method) (decent_bench.metrics.metric_library.SentMessagesDropped method) (decent_bench.metrics.metric_library.ServerAccuracy method) (decent_bench.metrics.metric_library.ServerMSE method) (decent_bench.metrics.metric_library.XError method) (decent_bench.metrics.metric_library.XUpdates method) (decent_bench.metrics.runtime_library.RuntimeConsensusError method) (decent_bench.metrics.runtime_library.RuntimeGradientNorm method) (decent_bench.metrics.runtime_library.RuntimeLoss method) (decent_bench.metrics.runtime_library.RuntimeRegret method) (decent_bench.metrics.RuntimeMetric method) compute_metrics() (in module decent_bench.benchmark) connected_agents() (decent_bench.metrics.NetworkMetricsView method) (decent_bench.networks.Network method) ConsensusError (class in decent_bench.metrics.metric_library) coordinator() (decent_bench.metrics.NetworkMetricsView method) (decent_bench.networks.FedNetwork method) copy() (in module decent_bench.utils.interoperability) Cost (class in decent_bench.costs) cost (decent_bench.agents.Agent property) (decent_bench.metrics.AgentMetricsView attribute) CPU (decent_bench.utils.types.SupportedDevices attribute) create_backup() (decent_bench.utils.checkpoint_manager.CheckpointManager method) create_classification_problem() (in module decent_bench.benchmark) create_figure() (decent_bench.metrics.RuntimeMetricPlotter method) create_quadratic_problem() (in module decent_bench.benchmark) create_regression_problem() (in module decent_bench.benchmark) CyclicActivation (class in decent_bench.schemes) D Datapoint (in module decent_bench.utils.types) dataset (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.EmpiricalRiskCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) Dataset (in module decent_bench.utils.types) DatasetHandler (class in decent_bench.datasets) DataSizeSelection (class in decent_bench.schemes) decent_bench module decent_bench.agents module decent_bench.algorithms module decent_bench.algorithms.federated module decent_bench.algorithms.p2p module decent_bench.algorithms.utils module decent_bench.benchmark module decent_bench.centralized_algorithms module decent_bench.costs module decent_bench.datasets module decent_bench.metrics module decent_bench.metrics.metric_library module decent_bench.metrics.runtime_library module decent_bench.metrics.utils module decent_bench.networks module decent_bench.schemes module decent_bench.utils module decent_bench.utils.agent_utils module decent_bench.utils.array module decent_bench.utils.checkpoint_manager module decent_bench.utils.interoperability module decent_bench.utils.logger module decent_bench.utils.network_utils module decent_bench.utils.progress_bar module decent_bench.utils.pytorch_utils module decent_bench.utils.types module DecentralizedLinearizedADMM (in module decent_bench.algorithms.p2p) degrees (decent_bench.networks.Network property) delta (decent_bench.algorithms.p2p.ATG attribute) description (decent_bench.metrics.Metric property) (decent_bench.metrics.metric_library.Accuracy attribute) (decent_bench.metrics.metric_library.ClientDriftFromServer attribute) (decent_bench.metrics.metric_library.ConsensusError attribute) (decent_bench.metrics.metric_library.FractionSelectedClients attribute) (decent_bench.metrics.metric_library.FunctionCalls attribute) (decent_bench.metrics.metric_library.GradientCalls attribute) (decent_bench.metrics.metric_library.GradientNorm attribute) (decent_bench.metrics.metric_library.HessianCalls attribute) (decent_bench.metrics.metric_library.Loss attribute) (decent_bench.metrics.metric_library.MSE attribute) (decent_bench.metrics.metric_library.Precision attribute) (decent_bench.metrics.metric_library.ProximalCalls attribute) (decent_bench.metrics.metric_library.Recall attribute) (decent_bench.metrics.metric_library.ReceivedMessages attribute) (decent_bench.metrics.metric_library.Regret attribute) (decent_bench.metrics.metric_library.SentMessages attribute) (decent_bench.metrics.metric_library.SentMessagesDropped attribute) (decent_bench.metrics.metric_library.ServerAccuracy attribute) (decent_bench.metrics.metric_library.ServerMSE attribute) (decent_bench.metrics.metric_library.XError attribute) (decent_bench.metrics.metric_library.XUpdates attribute) (decent_bench.metrics.runtime_library.RuntimeConsensusError attribute) (decent_bench.metrics.runtime_library.RuntimeGradientNorm attribute) (decent_bench.metrics.runtime_library.RuntimeLoss attribute) (decent_bench.metrics.runtime_library.RuntimeRegret attribute) (decent_bench.metrics.RuntimeMetric property) device (decent_bench.costs.BaseRegularizerCost property) (decent_bench.costs.Cost property) (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) (decent_bench.costs.QuadraticCost property) (decent_bench.costs.ScaledCost property) (decent_bench.costs.SumCost property) (decent_bench.costs.ZeroCost property) device_to_framework_device() (in module decent_bench.utils.interoperability) DGD (class in decent_bench.algorithms.p2p) diag() (in module decent_bench.utils.interoperability) DiNNO (class in decent_bench.algorithms.p2p) display_metrics() (in module decent_bench.benchmark) div() (in module decent_bench.utils.interoperability) DLM (class in decent_bench.algorithms.p2p) domain_shape (decent_bench.costs.Cost property) dot() (in module decent_bench.utils.interoperability) DropScheme (class in decent_bench.schemes) E ED (class in decent_bench.algorithms.p2p) edges (decent_bench.networks.Network property) EmpiricalRegularizedCost (class in decent_bench.costs) EmpiricalRiskBatchSize (in module decent_bench.utils.types) EmpiricalRiskCost (class in decent_bench.costs) EmpiricalRiskIndices (in module decent_bench.utils.types) EmpiricalRiskReduction (in module decent_bench.utils.types) epsilon (decent_bench.algorithms.federated.FedOpt attribute) evaluate() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRiskCost method) ExactDiffusion (in module decent_bench.algorithms.p2p) EXTRA (class in decent_bench.algorithms.p2p) eye() (in module decent_bench.utils.interoperability) eye_like() (in module decent_bench.utils.interoperability) F f (decent_bench.agents.Agent property) f() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRiskCost method) FairSelection (class in decent_bench.schemes) FedAdagrad (class in decent_bench.algorithms.federated) FedAdam (class in decent_bench.algorithms.federated) FedAlgorithm (class in decent_bench.algorithms.federated) FedAvg (class in decent_bench.algorithms.federated) FedDyn (class in decent_bench.algorithms.federated) FEDERATED (decent_bench.metrics.NetworkType attribute) FedLT (class in decent_bench.algorithms.federated) FedNetwork (class in decent_bench.networks) FedNova (class in decent_bench.algorithms.federated) FedOpt (class in decent_bench.algorithms.federated) FedPD (class in decent_bench.algorithms.federated) FedProx (class in decent_bench.algorithms.federated) FedYogi (class in decent_bench.algorithms.federated) fit_elbow_curve() (in module decent_bench.metrics.utils) forward() (decent_bench.utils.pytorch_utils.ArgmaxActivation method) (decent_bench.utils.pytorch_utils.ArgminActivation method) (decent_bench.utils.pytorch_utils.SimpleLinearModel method) FractionalQuadraticRegularizerCost (class in decent_bench.costs) FractionSelectedClients (class in decent_bench.metrics.metric_library) framework (decent_bench.costs.BaseRegularizerCost property) (decent_bench.costs.Cost property) (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) (decent_bench.costs.QuadraticCost property) (decent_bench.costs.ScaledCost property) (decent_bench.costs.SumCost property) (decent_bench.costs.ZeroCost property) framework_device_of_array() (in module decent_bench.utils.interoperability) from_agent() (decent_bench.metrics.AgentMetricsView static method) from_network() (decent_bench.metrics.NetworkMetricsView static method) function (decent_bench.metrics.ComputationalCost attribute) function() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.FractionalQuadraticRegularizerCost method) (decent_bench.costs.L1RegularizerCost method) (decent_bench.costs.L2RegularizerCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) (decent_bench.costs.QuadraticCost method) (decent_bench.costs.ScaledCost method) (decent_bench.costs.SumCost method) (decent_bench.costs.ZeroCost method) FunctionCalls (class in decent_bench.metrics.metric_library) G G (decent_bench.networks.Network property) gamma (decent_bench.algorithms.p2p.ATG attribute) GaussianNoise (class in decent_bench.schemes) get() (decent_bench.agents.ReceivedMessages method) (decent_bench.utils.logger.LogQueue method) get_completed_trials() (decent_bench.utils.checkpoint_manager.CheckpointManager method) get_datapoints() (decent_bench.datasets.DatasetHandler method) (decent_bench.datasets.KaggleDatasetHandler method) (decent_bench.datasets.PyTorchDatasetHandler method) (decent_bench.datasets.SyntheticClassificationDatasetHandler method) (decent_bench.datasets.SyntheticRegressionDatasetHandler method) get_handle() (decent_bench.utils.progress_bar.ProgressBarController method) get_item() (in module decent_bench.utils.interoperability) get_partitions() (decent_bench.datasets.DatasetHandler method) (decent_bench.datasets.KaggleDatasetHandler method) (decent_bench.datasets.PyTorchDatasetHandler method) (decent_bench.datasets.SyntheticClassificationDatasetHandler method) (decent_bench.datasets.SyntheticRegressionDatasetHandler method) get_results_path() (decent_bench.utils.checkpoint_manager.CheckpointManager method) get_rng_state() (in module decent_bench.utils.interoperability) get_seed() (in module decent_bench.utils.interoperability) get_x() (decent_bench.agents.AgentHistory method) GilbertElliott (class in decent_bench.schemes) GPU (decent_bench.utils.types.SupportedDevices attribute) gradient (decent_bench.metrics.ComputationalCost attribute) gradient() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.FractionalQuadraticRegularizerCost method) (decent_bench.costs.L1RegularizerCost method) (decent_bench.costs.L2RegularizerCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) (decent_bench.costs.QuadraticCost method) (decent_bench.costs.ScaledCost method) (decent_bench.costs.SumCost method) (decent_bench.costs.ZeroCost method) GradientCalls (class in decent_bench.metrics.metric_library) GradientDescent (class in decent_bench.centralized_algorithms) GradientNorm (class in decent_bench.metrics.metric_library) graph (decent_bench.metrics.NetworkMetricsView attribute) (decent_bench.networks.Network property) GT_SAGA (class in decent_bench.algorithms.p2p) GT_SARAH (class in decent_bench.algorithms.p2p) GT_VR (class in decent_bench.algorithms.p2p) H has() (decent_bench.agents.ReceivedMessages method) hessian (decent_bench.metrics.ComputationalCost attribute) hessian() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.FractionalQuadraticRegularizerCost method) (decent_bench.costs.L1RegularizerCost method) (decent_bench.costs.L2RegularizerCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) (decent_bench.costs.QuadraticCost method) (decent_bench.costs.ScaledCost method) (decent_bench.costs.SumCost method) (decent_bench.costs.ZeroCost method) HessianCalls (class in decent_bench.metrics.metric_library) HighLossSelection (class in decent_bench.schemes) I id (decent_bench.agents.Agent property) (decent_bench.metrics.AgentMetricsView attribute) index (decent_bench.agents.Agent property) infer_client_data_size() (in module decent_bench.utils.agent_utils) init_local_training() (decent_bench.costs.PyTorchCost method) initial_states() (in module decent_bench.algorithms.utils) initialize() (decent_bench.agents.Agent method) (decent_bench.algorithms.Algorithm method) (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.p2p.P2PAlgorithm method) (decent_bench.utils.checkpoint_manager.CheckpointManager method) initialize_plot() (decent_bench.metrics.RuntimeMetric method) InitialStates (in module decent_bench.utils.types) is_active() (decent_bench.schemes.AgentActivationScheme method) (decent_bench.schemes.AlwaysActive method) (decent_bench.schemes.CyclicActivation method) (decent_bench.schemes.MarkovChainActivation method) (decent_bench.schemes.PoissonActivation method) (decent_bench.schemes.UniformActivationRate method) is_available() (decent_bench.metrics.Metric method) (decent_bench.metrics.metric_library.Accuracy method) (decent_bench.metrics.metric_library.ClientDriftFromServer method) (decent_bench.metrics.metric_library.FractionSelectedClients method) (decent_bench.metrics.metric_library.MSE method) (decent_bench.metrics.metric_library.Precision method) (decent_bench.metrics.metric_library.Recall method) (decent_bench.metrics.metric_library.Regret method) (decent_bench.metrics.metric_library.ServerAccuracy method) (decent_bench.metrics.metric_library.ServerMSE method) (decent_bench.metrics.metric_library.XError method) is_benchmark_completed() (decent_bench.utils.checkpoint_manager.CheckpointManager method) is_benchmark_started() (decent_bench.utils.checkpoint_manager.CheckpointManager method) is_empty() (decent_bench.utils.checkpoint_manager.CheckpointManager method) is_supported_array_type() (in module decent_bench.utils.interoperability) is_trial_complete() (decent_bench.utils.checkpoint_manager.CheckpointManager method) items() (decent_bench.agents.AgentHistory method) iterations (decent_bench.algorithms.Algorithm attribute) (decent_bench.algorithms.federated.FedAlgorithm attribute) (decent_bench.algorithms.federated.FedAvg attribute) (decent_bench.algorithms.federated.FedDyn attribute) (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.federated.FedNova attribute) (decent_bench.algorithms.federated.FedOpt attribute) (decent_bench.algorithms.federated.FedPD attribute) (decent_bench.algorithms.federated.FedProx attribute) (decent_bench.algorithms.federated.Scaffold attribute) (decent_bench.algorithms.p2p.ADMM attribute) (decent_bench.algorithms.p2p.ATC attribute) (decent_bench.algorithms.p2p.ATC_Tracking attribute) (decent_bench.algorithms.p2p.ATG attribute) (decent_bench.algorithms.p2p.AugDGM attribute) (decent_bench.algorithms.p2p.DGD attribute) (decent_bench.algorithms.p2p.DiNNO attribute) (decent_bench.algorithms.p2p.DLM attribute) (decent_bench.algorithms.p2p.ED attribute) (decent_bench.algorithms.p2p.EXTRA attribute) (decent_bench.algorithms.p2p.GT_SAGA attribute) (decent_bench.algorithms.p2p.GT_SARAH attribute) (decent_bench.algorithms.p2p.GT_VR attribute) (decent_bench.algorithms.p2p.KGT attribute) (decent_bench.algorithms.p2p.LED attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) (decent_bench.algorithms.p2p.NIDS attribute) (decent_bench.algorithms.p2p.P2PAlgorithm attribute) (decent_bench.algorithms.p2p.ProxSkip attribute) (decent_bench.algorithms.p2p.SimpleGT attribute) (decent_bench.algorithms.p2p.WangElia attribute) (decent_bench.benchmark.MetricResult property) (decent_bench.metrics.NetworkMetricsView property) J JAX (decent_bench.utils.types.SupportedFrameworks attribute) K KaggleDatasetHandler (class in decent_bench.datasets) keys() (decent_bench.agents.AgentHistory method) KGT (class in decent_bench.algorithms.p2p) L L1RegularizerCost (class in decent_bench.costs) L2RegularizerCost (class in decent_bench.costs) LED (class in decent_bench.algorithms.p2p) linear_convergence_rate() (in module decent_bench.metrics.utils) LinearRegressionCost (class in decent_bench.costs) load_benchmark_problem() (decent_bench.utils.checkpoint_manager.CheckpointManager method) load_benchmark_result() (decent_bench.utils.checkpoint_manager.CheckpointManager method) load_checkpoint() (decent_bench.utils.checkpoint_manager.CheckpointManager method) load_initial_algorithms() (decent_bench.utils.checkpoint_manager.CheckpointManager method) load_metadata() (decent_bench.utils.checkpoint_manager.CheckpointManager method) load_metrics_result() (decent_bench.utils.checkpoint_manager.CheckpointManager method) load_trial_result() (decent_bench.utils.checkpoint_manager.CheckpointManager method) local_solver (decent_bench.algorithms.federated.FedLT attribute) local_training() (decent_bench.costs.PyTorchCost method) LocalSteps (in module decent_bench.utils.types) LOGGER (in module decent_bench.utils.logger) LogisticRegressionCost (class in decent_bench.costs) LogQueue (class in decent_bench.utils.logger) Loss (class in decent_bench.metrics.metric_library) loss (decent_bench.agents.Agent property) loss() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRiskCost method) LT_ADMM (class in decent_bench.algorithms.p2p) LT_ADMM_VR (class in decent_bench.algorithms.p2p) M m_cvx (decent_bench.costs.Cost property) (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.FractionalQuadraticRegularizerCost property) (decent_bench.costs.L1RegularizerCost property) (decent_bench.costs.L2RegularizerCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) (decent_bench.costs.QuadraticCost property) (decent_bench.costs.ScaledCost property) (decent_bench.costs.SumCost property) (decent_bench.costs.ZeroCost property) m_smooth (decent_bench.costs.Cost property) (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.FractionalQuadraticRegularizerCost property) (decent_bench.costs.L1RegularizerCost property) (decent_bench.costs.L2RegularizerCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) (decent_bench.costs.QuadraticCost property) (decent_bench.costs.ScaledCost property) (decent_bench.costs.SumCost property) (decent_bench.costs.ZeroCost property) make_noise() (decent_bench.schemes.GaussianNoise method) (decent_bench.schemes.NoiseScheme method) (decent_bench.schemes.NoNoise method) mark_one_trial_as_complete() (decent_bench.utils.progress_bar.ProgressBarController method) mark_trial_complete() (decent_bench.utils.checkpoint_manager.CheckpointManager method) MarkovChainActivation (class in decent_bench.schemes) matmul() (in module decent_bench.utils.interoperability) max() (decent_bench.agents.AgentHistory method) (in module decent_bench.utils.interoperability) maximum() (in module decent_bench.utils.interoperability) mean() (in module decent_bench.utils.interoperability) message() (decent_bench.agents.Agent method) messages() (decent_bench.agents.Agent method) Metric (class in decent_bench.metrics) MetricResult (class in decent_bench.benchmark) min() (decent_bench.agents.AgentHistory method) (in module decent_bench.utils.interoperability) module decent_bench decent_bench.agents decent_bench.algorithms decent_bench.algorithms.federated decent_bench.algorithms.p2p decent_bench.algorithms.utils decent_bench.benchmark decent_bench.centralized_algorithms decent_bench.costs decent_bench.datasets decent_bench.metrics decent_bench.metrics.metric_library decent_bench.metrics.runtime_library decent_bench.metrics.utils decent_bench.networks decent_bench.schemes decent_bench.utils decent_bench.utils.agent_utils decent_bench.utils.array decent_bench.utils.checkpoint_manager decent_bench.utils.interoperability decent_bench.utils.logger decent_bench.utils.network_utils decent_bench.utils.progress_bar decent_bench.utils.pytorch_utils decent_bench.utils.types momentum (decent_bench.algorithms.federated.FedNova attribute) MPS (decent_bench.utils.types.SupportedDevices attribute) MSE (class in decent_bench.metrics.metric_library) mul() (in module decent_bench.utils.interoperability) N n_features (decent_bench.datasets.DatasetHandler property) (decent_bench.datasets.KaggleDatasetHandler property) (decent_bench.datasets.PyTorchDatasetHandler property) (decent_bench.datasets.SyntheticClassificationDatasetHandler property) (decent_bench.datasets.SyntheticRegressionDatasetHandler property) n_function_calls (decent_bench.metrics.AgentMetricsView attribute) n_gradient_calls (decent_bench.metrics.AgentMetricsView attribute) n_hessian_calls (decent_bench.metrics.AgentMetricsView attribute) n_partitions (decent_bench.datasets.DatasetHandler property) (decent_bench.datasets.KaggleDatasetHandler property) (decent_bench.datasets.PyTorchDatasetHandler property) (decent_bench.datasets.SyntheticClassificationDatasetHandler property) (decent_bench.datasets.SyntheticRegressionDatasetHandler property) n_proximal_calls (decent_bench.metrics.AgentMetricsView attribute) n_received_messages (decent_bench.metrics.AgentMetricsView attribute) n_samples (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.EmpiricalRiskCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) (decent_bench.datasets.DatasetHandler property) (decent_bench.datasets.KaggleDatasetHandler property) (decent_bench.datasets.PyTorchDatasetHandler property) (decent_bench.datasets.SyntheticClassificationDatasetHandler property) (decent_bench.datasets.SyntheticRegressionDatasetHandler property) n_sent_messages (decent_bench.metrics.AgentMetricsView attribute) n_sent_messages_dropped (decent_bench.metrics.AgentMetricsView attribute) n_targets (decent_bench.datasets.DatasetHandler property) (decent_bench.datasets.KaggleDatasetHandler property) (decent_bench.datasets.PyTorchDatasetHandler property) (decent_bench.datasets.SyntheticClassificationDatasetHandler property) (decent_bench.datasets.SyntheticRegressionDatasetHandler property) n_times_selected (decent_bench.metrics.AgentMetricsView attribute) n_x_updates (decent_bench.metrics.AgentMetricsView attribute) name (decent_bench.algorithms.Algorithm property) (decent_bench.algorithms.federated.FedAdagrad attribute) (decent_bench.algorithms.federated.FedAdam attribute) (decent_bench.algorithms.federated.FedAlgorithm property) (decent_bench.algorithms.federated.FedAvg attribute) (decent_bench.algorithms.federated.FedDyn attribute) (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.federated.FedNova attribute) (decent_bench.algorithms.federated.FedOpt attribute) (decent_bench.algorithms.federated.FedPD attribute) (decent_bench.algorithms.federated.FedProx attribute) (decent_bench.algorithms.federated.FedYogi attribute) (decent_bench.algorithms.federated.Scaffold attribute) (decent_bench.algorithms.p2p.ADMM attribute) (decent_bench.algorithms.p2p.ATC attribute) (decent_bench.algorithms.p2p.ATC_Tracking attribute) (decent_bench.algorithms.p2p.ATG attribute) (decent_bench.algorithms.p2p.AugDGM attribute) (decent_bench.algorithms.p2p.DGD attribute) (decent_bench.algorithms.p2p.DiNNO attribute) (decent_bench.algorithms.p2p.DLM attribute) (decent_bench.algorithms.p2p.ED attribute) (decent_bench.algorithms.p2p.EXTRA attribute) (decent_bench.algorithms.p2p.GT_SAGA attribute) (decent_bench.algorithms.p2p.GT_SARAH attribute) (decent_bench.algorithms.p2p.GT_VR attribute) (decent_bench.algorithms.p2p.KGT attribute) (decent_bench.algorithms.p2p.LED attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) (decent_bench.algorithms.p2p.LT_ADMM_VR attribute) (decent_bench.algorithms.p2p.NIDS attribute) (decent_bench.algorithms.p2p.P2PAlgorithm property) (decent_bench.algorithms.p2p.ProxSkip attribute) (decent_bench.algorithms.p2p.SimpleGT attribute) (decent_bench.algorithms.p2p.WangElia attribute) negative() (in module decent_bench.utils.interoperability) neighbors() (decent_bench.metrics.NetworkMetricsView method) (decent_bench.networks.P2PNetwork method) Network (class in decent_bench.networks) network (decent_bench.benchmark.BenchmarkProblem attribute) network_type (decent_bench.metrics.NetworkMetricsView attribute) network_views (decent_bench.benchmark.MetricResult attribute) NetworkMetricsView (class in decent_bench.metrics) NetworkT (built-in class) NetworkType (class in decent_bench.metrics) NEXT (in module decent_bench.algorithms.p2p) NIDS (class in decent_bench.algorithms.p2p) no_count() (decent_bench.agents.Agent static method) NoCompression (class in decent_bench.schemes) NoDrops (class in decent_bench.schemes) NoiseScheme (class in decent_bench.schemes) NoNoise (class in decent_bench.schemes) norm() (in module decent_bench.utils.interoperability) normal() (in module decent_bench.utils.interoperability) normal_initialization() (in module decent_bench.algorithms.utils) normal_like() (in module decent_bench.utils.interoperability) num_local_steps (decent_bench.algorithms.federated.FedAlgorithm attribute) (decent_bench.algorithms.federated.FedAvg attribute) (decent_bench.algorithms.federated.FedDyn attribute) (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.federated.FedNova attribute) (decent_bench.algorithms.federated.FedOpt attribute) (decent_bench.algorithms.federated.FedPD attribute) (decent_bench.algorithms.federated.FedProx attribute) (decent_bench.algorithms.federated.Scaffold attribute) (decent_bench.algorithms.p2p.DiNNO attribute) (decent_bench.algorithms.p2p.GT_SARAH attribute) (decent_bench.algorithms.p2p.KGT attribute) (decent_bench.algorithms.p2p.LED attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) NUMPY (decent_bench.utils.types.SupportedFrameworks attribute) O ones_like() (in module decent_bench.utils.interoperability) P P2P (decent_bench.metrics.NetworkType attribute) P2PAlgorithm (class in decent_bench.algorithms.p2p) P2PNetwork (class in decent_bench.networks) penalty (decent_bench.algorithms.federated.FedDyn attribute) (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.federated.FedNova attribute) (decent_bench.algorithms.federated.FedPD attribute) (decent_bench.algorithms.federated.FedProx attribute) (decent_bench.algorithms.p2p.ADMM attribute) (decent_bench.algorithms.p2p.ATG attribute) (decent_bench.algorithms.p2p.DiNNO attribute) (decent_bench.algorithms.p2p.DLM attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) plot_metrics (decent_bench.benchmark.MetricResult property) plot_network() (in module decent_bench.utils.network_utils) plot_results (decent_bench.benchmark.MetricResult attribute) PoissonActivation (class in decent_bench.schemes) power() (in module decent_bench.utils.interoperability) Precision (class in decent_bench.metrics.metric_library) predict() (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) problem (decent_bench.benchmark.BenchmarkResult attribute) ProgressBarController (class in decent_bench.utils.progress_bar) ProgressBarHandle (class in decent_bench.utils.progress_bar) proximal (decent_bench.metrics.ComputationalCost attribute) proximal() (decent_bench.costs.Cost method) (decent_bench.costs.EmpiricalRegularizedCost method) (decent_bench.costs.EmpiricalRiskCost method) (decent_bench.costs.FractionalQuadraticRegularizerCost method) (decent_bench.costs.L1RegularizerCost method) (decent_bench.costs.L2RegularizerCost method) (decent_bench.costs.LinearRegressionCost method) (decent_bench.costs.LogisticRegressionCost method) (decent_bench.costs.PyTorchCost method) (decent_bench.costs.QuadraticCost method) (decent_bench.costs.ScaledCost method) (decent_bench.costs.SumCost method) (decent_bench.costs.ZeroCost method) proximal_solver() (in module decent_bench.centralized_algorithms) ProximalCalls (class in decent_bench.metrics.metric_library) ProxSkip (class in decent_bench.algorithms.p2p) put() (decent_bench.agents.ReceivedMessages method) put_nowait() (decent_bench.utils.logger.LogQueue method) PYTORCH (decent_bench.utils.types.SupportedFrameworks attribute) pytorch_initialization() (in module decent_bench.algorithms.utils) PyTorchCost (class in decent_bench.costs) PyTorchDatasetHandler (class in decent_bench.datasets) Q QuadraticCost (class in decent_bench.costs) Quantization (class in decent_bench.schemes) R RandK (class in decent_bench.schemes) raw_plot_results (decent_bench.benchmark.MetricResult attribute) raw_table_results (decent_bench.benchmark.MetricResult attribute) Recall (class in decent_bench.metrics.metric_library) ReceivedMessages (class in decent_bench.agents) (class in decent_bench.metrics.metric_library) Regret (class in decent_bench.metrics.metric_library) relaxation (decent_bench.algorithms.p2p.ADMM attribute) (decent_bench.algorithms.p2p.ATG attribute) reshape() (in module decent_bench.utils.interoperability) resume_benchmark() (in module decent_bench.benchmark) rng_jax() (in module decent_bench.utils.interoperability) rng_numpy() (in module decent_bench.utils.interoperability) rng_tensorflow() (in module decent_bench.utils.interoperability) rng_torch() (in module decent_bench.utils.interoperability) run() (decent_bench.algorithms.Algorithm method) (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.p2p.P2PAlgorithm method) (decent_bench.centralized_algorithms.Solver method) (decent_bench.metrics.RuntimeMetricPlotter method) RuntimeConsensusError (class in decent_bench.metrics.runtime_library) RuntimeGradientNorm (class in decent_bench.metrics.runtime_library) RuntimeLoss (class in decent_bench.metrics.runtime_library) RuntimeMetric (class in decent_bench.metrics) RuntimeMetricPlotter (class in decent_bench.metrics) RuntimeRegret (class in decent_bench.metrics.runtime_library) S save_checkpoint() (decent_bench.utils.checkpoint_manager.CheckpointManager method) save_metrics_result() (decent_bench.utils.checkpoint_manager.CheckpointManager method) Scaffold (class in decent_bench.algorithms.federated) ScaledCost (class in decent_bench.costs) select() (decent_bench.schemes.ClientSelectionScheme method) (decent_bench.schemes.DataSizeSelection method) (decent_bench.schemes.FairSelection method) (decent_bench.schemes.HighLossSelection method) (decent_bench.schemes.UniformSelection method) select_clients() (decent_bench.algorithms.federated.FedAlgorithm method) selection_scheme (decent_bench.algorithms.federated.FedAlgorithm attribute) send() (decent_bench.networks.FedNetwork method) (decent_bench.networks.Network method) SentMessages (class in decent_bench.metrics.metric_library) SentMessagesDropped (class in decent_bench.metrics.metric_library) server() (decent_bench.metrics.NetworkMetricsView method) (decent_bench.networks.FedNetwork method) server_broadcast() (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.federated.Scaffold method) server_momentum (decent_bench.algorithms.federated.FedNova attribute) server_step_size (decent_bench.algorithms.federated.FedOpt attribute) (decent_bench.algorithms.federated.Scaffold attribute) ServerAccuracy (class in decent_bench.metrics.metric_library) ServerMSE (class in decent_bench.metrics.metric_library) set_item() (in module decent_bench.utils.interoperability) set_rng_state() (in module decent_bench.utils.interoperability) set_seed() (in module decent_bench.utils.interoperability) set_x() (decent_bench.agents.AgentHistory method) shape (decent_bench.costs.BaseRegularizerCost property) (decent_bench.costs.Cost property) (decent_bench.costs.EmpiricalRegularizedCost property) (decent_bench.costs.LinearRegressionCost property) (decent_bench.costs.LogisticRegressionCost property) (decent_bench.costs.PyTorchCost property) (decent_bench.costs.QuadraticCost property) (decent_bench.costs.ScaledCost property) (decent_bench.costs.SumCost property) (decent_bench.costs.ZeroCost property) shape() (in module decent_bench.utils.interoperability) should_checkpoint() (decent_bench.utils.checkpoint_manager.CheckpointManager method) should_drop() (decent_bench.schemes.DropScheme method) (decent_bench.schemes.GilbertElliott method) (decent_bench.schemes.NoDrops method) (decent_bench.schemes.UniformDropRate method) should_update() (decent_bench.metrics.RuntimeMetric method) shutdown() (decent_bench.metrics.RuntimeMetricPlotter method) sign() (in module decent_bench.utils.interoperability) SimpleGradientTracking (in module decent_bench.algorithms.p2p) SimpleGT (class in decent_bench.algorithms.p2p) SimpleLinearModel (class in decent_bench.utils.pytorch_utils) size (decent_bench.costs.Cost property) skip_probability (decent_bench.algorithms.federated.FedPD attribute) snapshot_agents() (decent_bench.networks.Network method) snapshot_prob (decent_bench.algorithms.p2p.GT_VR attribute) solve() (in module decent_bench.centralized_algorithms) Solver (class in decent_bench.centralized_algorithms) solver_args (decent_bench.algorithms.federated.FedLT attribute) SONATA (in module decent_bench.algorithms.p2p) sqrt() (in module decent_bench.utils.interoperability) squeeze() (in module decent_bench.utils.interoperability) stack() (in module decent_bench.utils.interoperability) start() (decent_bench.metrics.RuntimeMetricPlotter method) start_log_listener() (in module decent_bench.utils.logger) start_logger() (in module decent_bench.utils.logger) start_progress_bar() (decent_bench.utils.progress_bar.ProgressBarHandle method) start_queue_logger() (in module decent_bench.utils.logger) state_snapshot_period (decent_bench.agents.Agent property) states (decent_bench.benchmark.BenchmarkResult attribute) step() (decent_bench.algorithms.Algorithm method) (decent_bench.algorithms.federated.FedAlgorithm method) (decent_bench.algorithms.p2p.P2PAlgorithm method) (decent_bench.centralized_algorithms.AcceleratedGradientDescent method) (decent_bench.centralized_algorithms.GradientDescent method) (decent_bench.centralized_algorithms.Solver method) step_size (decent_bench.algorithms.federated.FedAvg attribute) (decent_bench.algorithms.federated.FedDyn attribute) (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.federated.FedNova attribute) (decent_bench.algorithms.federated.FedOpt attribute) (decent_bench.algorithms.federated.FedPD attribute) (decent_bench.algorithms.federated.FedProx attribute) (decent_bench.algorithms.federated.Scaffold attribute) (decent_bench.algorithms.p2p.ATC attribute) (decent_bench.algorithms.p2p.ATC_Tracking attribute) (decent_bench.algorithms.p2p.AugDGM attribute) (decent_bench.algorithms.p2p.DGD attribute) (decent_bench.algorithms.p2p.DiNNO attribute) (decent_bench.algorithms.p2p.DLM attribute) (decent_bench.algorithms.p2p.ED attribute) (decent_bench.algorithms.p2p.EXTRA attribute) (decent_bench.algorithms.p2p.GT_SAGA attribute) (decent_bench.algorithms.p2p.GT_SARAH attribute) (decent_bench.algorithms.p2p.GT_VR attribute) (decent_bench.algorithms.p2p.KGT attribute) (decent_bench.algorithms.p2p.LED attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) (decent_bench.algorithms.p2p.NIDS attribute) (decent_bench.algorithms.p2p.ProxSkip attribute) (decent_bench.algorithms.p2p.SimpleGT attribute) (decent_bench.algorithms.p2p.WangElia attribute) StochasticQuantization (class in decent_bench.schemes) stop() (decent_bench.utils.progress_bar.ProgressBarController method) sub() (in module decent_bench.utils.interoperability) sum() (in module decent_bench.utils.interoperability) SumCost (class in decent_bench.costs) SupportedArrayTypes (in module decent_bench.utils.types) SupportedDevices (class in decent_bench.utils.types) SupportedFrameworks (class in decent_bench.utils.types) SyntheticClassificationDatasetHandler (class in decent_bench.datasets) SyntheticRegressionDatasetHandler (class in decent_bench.datasets) T table_metrics (decent_bench.benchmark.MetricResult property) table_results (decent_bench.benchmark.MetricResult attribute) TENSORFLOW (decent_bench.utils.types.SupportedFrameworks attribute) test_data (decent_bench.benchmark.BenchmarkProblem attribute) to_array() (in module decent_bench.utils.interoperability) to_array_like() (in module decent_bench.utils.interoperability) to_jax() (in module decent_bench.utils.interoperability) to_numpy() (in module decent_bench.utils.interoperability) to_tensorflow() (in module decent_bench.utils.interoperability) to_torch() (in module decent_bench.utils.interoperability) TopK (class in decent_bench.schemes) transpose() (in module decent_bench.utils.interoperability) U uniform() (in module decent_bench.utils.interoperability) uniform_initialization() (in module decent_bench.algorithms.utils) uniform_like() (in module decent_bench.utils.interoperability) UniformActivationRate (class in decent_bench.schemes) UniformDropRate (class in decent_bench.schemes) UniformSelection (class in decent_bench.schemes) unmark_trial_complete() (decent_bench.utils.checkpoint_manager.CheckpointManager method) update() (decent_bench.metrics.RuntimeMetricPlotter method) update_interval (decent_bench.metrics.RuntimeMetric property) update_plot() (decent_bench.metrics.RuntimeMetric method) update_table_results() (decent_bench.benchmark.MetricResult method) use_momentum (decent_bench.algorithms.federated.FedNova attribute) use_prox (decent_bench.algorithms.federated.FedNova attribute) use_server_momentum (decent_bench.algorithms.federated.FedNova attribute) V v2 (decent_bench.algorithms.p2p.LT_ADMM_VR attribute) values() (decent_bench.agents.AgentHistory method) W WangElia (class in decent_bench.algorithms.p2p) weights (decent_bench.networks.P2PNetwork property) X x (decent_bench.agents.Agent property) x0 (decent_bench.algorithms.federated.FedAvg attribute) (decent_bench.algorithms.federated.FedDyn attribute) (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.federated.FedNova attribute) (decent_bench.algorithms.federated.FedOpt attribute) (decent_bench.algorithms.federated.FedPD attribute) (decent_bench.algorithms.federated.FedProx attribute) (decent_bench.algorithms.federated.Scaffold attribute) (decent_bench.algorithms.p2p.ADMM attribute) (decent_bench.algorithms.p2p.ATC attribute) (decent_bench.algorithms.p2p.ATC_Tracking attribute) (decent_bench.algorithms.p2p.ATG attribute) (decent_bench.algorithms.p2p.AugDGM attribute) (decent_bench.algorithms.p2p.DGD attribute) (decent_bench.algorithms.p2p.DiNNO attribute) (decent_bench.algorithms.p2p.DLM attribute) (decent_bench.algorithms.p2p.ED attribute) (decent_bench.algorithms.p2p.EXTRA attribute) (decent_bench.algorithms.p2p.GT_SAGA attribute) (decent_bench.algorithms.p2p.GT_SARAH attribute) (decent_bench.algorithms.p2p.GT_VR attribute) (decent_bench.algorithms.p2p.KGT attribute) (decent_bench.algorithms.p2p.LED attribute) (decent_bench.algorithms.p2p.LT_ADMM attribute) (decent_bench.algorithms.p2p.NIDS attribute) (decent_bench.algorithms.p2p.ProxSkip attribute) (decent_bench.algorithms.p2p.SimpleGT attribute) (decent_bench.algorithms.p2p.WangElia attribute) x_history (decent_bench.metrics.AgentMetricsView attribute) x_log (decent_bench.metrics.runtime_library.RuntimeConsensusError attribute) (decent_bench.metrics.runtime_library.RuntimeGradientNorm attribute) (decent_bench.metrics.runtime_library.RuntimeLoss attribute) (decent_bench.metrics.runtime_library.RuntimeRegret attribute) (decent_bench.metrics.RuntimeMetric property) x_mean() (in module decent_bench.metrics.utils) x_optimal (decent_bench.benchmark.BenchmarkProblem attribute) XError (class in decent_bench.metrics.metric_library) XUpdates (class in decent_bench.metrics.metric_library) Y y_log (decent_bench.metrics.runtime_library.RuntimeConsensusError attribute) (decent_bench.metrics.runtime_library.RuntimeGradientNorm attribute) (decent_bench.metrics.runtime_library.RuntimeLoss attribute) (decent_bench.metrics.runtime_library.RuntimeRegret attribute) (decent_bench.metrics.RuntimeMetric property) Z z0 (decent_bench.algorithms.federated.FedLT attribute) (decent_bench.algorithms.p2p.ADMM attribute) (decent_bench.algorithms.p2p.ATG attribute) ZeroCost (class in decent_bench.costs) zeros() (in module decent_bench.utils.interoperability) zeros_like() (in module decent_bench.utils.interoperability)
