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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/SforAiDl/lrnnx/llms.txt

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Overview

Discretization functions convert continuous-time state space models to discrete-time representations. These functions are essential for implementing recurrent neural networks based on continuous-time dynamical systems.

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DISCRETIZE_FNS

DISCRETIZE_FNS: dict[str, Callable]
Dictionary mapping discretization method names to their corresponding functions. Used internally by the LRNN base class to select the appropriate discretization method. Available methods:
  • "zoh" → zoh - Zero-Order Hold
  • "bilinear" → bilinear - Bilinear transform
  • "dirac" → dirac - Dirac discretization
  • "async" → async_ - Asynchronous discretization
  • "no_discretization" → no_discretization - Identity operation

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Discretization Functions

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zoh

zoh(A, delta, integration_timesteps=None) -> tuple[Tensor, Tensor]
Zero-Order Hold (ZOH) discretization method, the most commonly used discretization across models. Mathematical formulation: Aˉ=exp⁡(ΔA)\bar{A} = \exp(\Delta A) γˉ=A−1(Aˉ−I)\bar{\gamma} = A^{-1} (\bar{A} - I) Reference: S4 Blog Post
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A
torch.Tensor
required
The continuous-time state matrix.
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delta
torch.Tensor
required
The discretization step size.
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integration_timesteps
torch.Tensor
Not used in ZOH discretization. Defaults to None.
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A_bar
torch.Tensor
The discretized system matrix.
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gamma_bar
torch.Tensor
The input normalizer.

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bilinear

bilinear(A, delta, integration_timesteps=None) -> tuple[Tensor, Tensor]
Bilinear transform method first introduced in the S4 model. Mathematical formulation: Aˉ=(I+0.5ΔA)−1(I−0.5ΔA)\bar{A} = (I + 0.5 \Delta A)^{-1} (I - 0.5 \Delta A) γˉ=(I+0.5ΔA)−1Δ\bar{\gamma} = (I + 0.5 \Delta A)^{-1} \Delta Reference: S4 Blog Post
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A
torch.Tensor
required
Continuous-time system matrix, shape (N,). Only diagonal elements are used.
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delta
torch.Tensor
required
Time step for discretization.
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integration_timesteps
torch.Tensor
Not used in bilinear discretization. Defaults to None.
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A_bar
torch.Tensor
The discretized system matrix.
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gamma_bar
torch.Tensor
The input normalizer.

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dirac

dirac(A, delta, integration_timesteps=None) -> tuple[Tensor, float]
Dirac discretization method. Mathematical formulation: Aˉ=exp⁡(ΔA)\bar{A} = \exp(\Delta A) γˉ=1.0\bar{\gamma} = 1.0 Reference: Event-SSM GitHub
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A
torch.Tensor
required
Continuous-time system matrix.
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delta
torch.Tensor
required
Time step for discretization.
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integration_timesteps
torch.Tensor
Not used in dirac discretization. Defaults to None.
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A_bar
torch.Tensor
The discretized system matrix.
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gamma_bar
float
The input normalizer, fixed at 1.0.

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async_

async_(A, delta, integration_timesteps) -> tuple[Tensor, Tensor]
Asynchronous discretization method for event-driven models. Introduced in arxiv:2404.18508 to provide a strong inductive bias for asynchronous event streams. Mathematical formulation: Aˉ=exp⁡(Δ⋅integration_timesteps⋅A)\bar{A} = \exp(\Delta \cdot \text{integration\_timesteps} \cdot A) γˉ=A−1(exp⁡(ΔA)−I)\bar{\gamma} = A^{-1} (\exp(\Delta A) - I)
This method exists primarily for legacy reasons. It is not possible to use this method (or any discretization with async timesteps) with LTI models.
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A
torch.Tensor
required
Continuous-time system matrix.
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delta
torch.Tensor
required
Time step for discretization.
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integration_timesteps
torch.Tensor
required
Timesteps for async discretization, ideally of shape (B, L), representing the difference in timesteps between events.
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A_bar
torch.Tensor
The discretized system matrix.
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gamma_bar
torch.Tensor
The input normalizer.

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no_discretization

no_discretization(A, delta, integration_timesteps=None) -> tuple[Tensor, float]
Identity operation that performs no discretization. Mathematical formulation: Aˉ=A\bar{A} = A γˉ=1.0\bar{\gamma} = 1.0
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A
torch.Tensor
required
Continuous-time system matrix (returned unchanged).
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delta
torch.Tensor
required
Time step for discretization (unused).
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integration_timesteps
torch.Tensor
Not used in no_discretization. Defaults to None.
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A_bar
torch.Tensor
Same as input A.
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gamma_bar
float
Fixed at 1.0, as B_bar = B.

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Example Usage

import torch
from lrnnx.core.discretization import zoh, bilinear, DISCRETIZE_FNS

# Direct function call
A = torch.randn(64)  # State matrix
delta = torch.tensor(0.01)  # Time step
A_bar, gamma_bar = zoh(A, delta)

# Using the dictionary
discretize_fn = DISCRETIZE_FNS["bilinear"]
A_bar, gamma_bar = discretize_fn(A, delta)

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