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Fast low-rank updates (LRU) of matrix determinants and pfaffians in JAX

lrux 최신버젼 다운로드

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<p align="center">
Fast low-rank updates (LRU) of determinants and Pfaffians in 
<a href="https://github.com/jax-ml/jax">JAX</a>
</p>

<p align="center">
<a href="https://chenao-phys.github.io/lrux/">📖 Documentation</a> •
<a href="https://arxiv.org/abs/2602.05255">📄 Paper</a>
</p>

---

## Overview

In quantum many-body physics and related numerical fields, one frequently encounters situations where a matrix  
$\mathbf{A}_1$ differs only slightly from a previously processed matrix $\mathbf{A}_0$.

Recomputing determinants or Pfaffians from scratch scales as $\mathcal{O}(n^3)$ and quickly becomes a bottleneck.  
**lrux** provides efficient **low-rank update (LRU)** routines that reduce the cost to $\mathcal{O}(n^2)$ per update, while remaining fully compatible with JAX transformations (`jit`, `vmap`, autodiff).

Typical applications include:

- Variational Monte Carlo (VMC)
- Auxiliary-field and determinantal QMC
- Neural network backflow / hidden fermion
- Any workflow involving repeated local matrix updates

---

## What's low-rank update?

In quantum physics and many other fields, it often happens that we have computed $\det(\mathbf{A}_0)$ and need to compute $\det(\mathbf{A}_1)$, where $\mathbf{A}_0$ and $\mathbf{A}_1$ are nearly identical. Due to the similarity of $\mathbf{A}_0$ and $\mathbf{A}_1$, we can well expect that in many cases $\det(\mathbf{A}_1)$ doesn't have to be recomputed from scratch.

Consider a special case that $\mathbf{A}_0$ and $\mathbf{A}_1$ are only different by one row. We can express their difference as

<div align="center">
  <img src="./images/A1_A0.svg"/>
</div>

Then $\mathbf{A}_1$ can be viewed as a low-rank update to $\mathbf{A}_0$, and the [matrix determinant lemma](https://en.wikipedia.org/wiki/Matrix_determinant_lemma) tells us

<div align="center">
  <img src="./images/detA1.svg"/>
</div>

If $\mathbf{A}_0^{-1}$ and $\det(\mathbf{A}_0)$ has been computed and stored earlier, one can immediately obtain $\det(\mathbf{A}_1)$ with $\mathcal{O}(n^2)$ complexity for any general $\mathbf{u}$ and $\mathbf{v}$, instead of the original determinant complexity $\mathcal{O}(n^3)$. The following code shows how this is done with lrux, where `lrux.det_lru` returns the ratio 

<div align="center">
  <img src="./images/ratio.svg"/>
</div>

```python
import jax

# 64-bit recommended for numerical precision
jax.config.update("jax_enable_x64", True)

import jax.numpy as jnp
import jax.random as jr
from lrux import det_lru

n = 10
A0 = jr.normal(jr.key(0), (n, n))
u = jr.normal(jr.key(1), (n,))
v = 5  # one-hot vector index
detA0 = jnp.linalg.det(A0)
Ainv = jnp.linalg.inv(A0)

ratio = det_lru(Ainv, u, v)
detA1_lru = detA0 * ratio

A1 = A0.at[v, :].add(u)
assert jnp.isclose(detA1_lru, jnp.linalg.det(A1))
```

---

## Consecutive updates

Sometimes we need to keep computing $\det(\mathbf{A}_2)$ by a low-rank update of $\mathbf{A}_1$, in which case $\mathbf{A}_1^{-1}$ is required. Utilizing the [Sherman–Morrison formula](https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula), one can also obtain the low-rank update of matrix inverse as

<div align="center">
  <img src="./images/A1inv.svg"/>
</div>

where the complexity is again $\mathcal{O}(n^2)$ instead of $\mathcal{O}(n^3)$. Following the previous example code, one can add a few lines below to perform consecutive low-rank updates.

```python
ratio, Ainv = det_lru(Ainv, u, v, return_update=True)
assert jnp.allclose(Ainv, jnp.linalg.inv(A1))

u_new = jr.normal(jr.key(2), (n,))
v_new = 6

ratio, Ainv = det_lru(Ainv, u_new, v_new, return_update=True)
detA2_lru = detA1_lru * ratio

A2 = A1.at[v_new, :].add(u_new)
assert jnp.isclose(detA2_lru, jnp.linalg.det(A2))
assert jnp.allclose(Ainv, jnp.linalg.inv(A2))
```

---

## What does lrux provide?

The main functions of lrux include `det_lru`, `det_lru_delayed`, `pf_lru`, and `pf_lru_delayed`. They provide:

- Row and column updates
- General rank-k updates
- Delayed updates
- `jit` and `vmap` compatibility

As the [pfaffian](https://en.wikipedia.org/wiki/Pfaffian) is not directly supported in JAX, we also provide backward-compatible functions `pf` and `slogpf` for pfaffian computations.

---

## Installation

Requires Python 3.8+ and JAX 0.4.4+

```
pip install lrux
```

---

## Citation

If you use lrux in your research, please cite:

```
@misc{Chen_lrux,
      title={lrux: Fast low-rank updates of determinants and Pfaffians in JAX}, 
      author={Ao Chen and Christopher Roth},
      year={2026},
      eprint={2602.05255},
      archivePrefix={arXiv},
      primaryClass={cond-mat.str-el},
      url={https://arxiv.org/abs/2602.05255}, 
}
```

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