diff --git a/docs/api/solvers/sde_solvers.md b/docs/api/solvers/sde_solvers.md index 41fd72ba..9e03db6d 100644 --- a/docs/api/solvers/sde_solvers.md +++ b/docs/api/solvers/sde_solvers.md @@ -1,7 +1,7 @@ # SDE solvers See also [How to choose a solver](../../usage/how-to-choose-a-solver.md#stochastic-differential-equations) -and [SDE example](../../devdocs/sde_example.ipynb/#table-of-available-srk-methods-in-diffrax) which contains a table of SDE solvers and their properties. +and [SDE example](../../../examples/sde_example.ipynb/#table-of-available-srk-methods-in-diffrax) which contains a table of SDE solvers and their properties. !!! info "Term structure" diff --git a/docs/devdocs/SDE_solver_table.md b/docs/devdocs/SDE_solver_table.md new file mode 100644 index 00000000..1c12def2 --- /dev/null +++ b/docs/devdocs/SDE_solver_table.md @@ -0,0 +1,44 @@ +# SDE solver table + +For an explanation of the terms in the table, see [how to choose a solver](../usage/how-to-choose-a-solver.md#stochastic-differential-equations). + +``` ++----------------+-------+------------+------------------------------------+-------------------+----------------+------------------------------------------+ +| | SDE | Lévy | Strong/weak order (per noise type) | VF evaluations | Embedded error | Recommended for | +| | type | area +----------+--------------+----------+-------+-----------+ estimation | (and other notes) | +| | | | General | Commutative | Additive | Drift | Diffusion | | | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| Euler | Itô | BM only | 0.5/1.0 | 0.5/1.0 | 1.0/1.0 | 1 | 1 | No | Itô SDEs when a cheap solver is needed. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| Heun | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | Standard solver for Stratonovich SDEs. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| EulerHeun | Strat | BM only | 0.5/1.0 | 0.5/1.0 | 1.0/1.0 | 1 | 2 | No | Stratonovich SDEs with expensive drift. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| ItoMilstein | Itô | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 1 | 1 | No | Better than Euler for Itô SDEs, but | +| | | | | | | | | | comuptes the derivative of diffusion VF. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| Stratonovich | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 1 | 1 | No | For commutative Stratonovich SDEs when | +| Milstein | | | | | | | | | space-time Lévy area is not available. | +| | | | | | | | | | Computes derivative of diffusion VF. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| ReversibleHeun | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | When a reversible solver is needed. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| Midpoint | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | Usually Heun should be preferred. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| Ralston | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | Usually Heun should be preferred. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| ShARK | Strat | space-time | / | / | 1.5/2.0 | 2 | 2 | Yes | Additive noise SDEs. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| SRA1 | Strat | space-time | / | / | 1.5/2.0 | 2 | 2 | Yes | Only slightly worse than ShARK. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| SEA | Strat | space-time | / | / | 1.0/1.0 | 1 | 1 | No | Cheap solver for additive noise SDEs. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| SPaRK | Strat | space-time | 0.5/1.0 | 1.0/1.0 | 1.5/2.0 | 3 | 3 | Yes | General SDEs when embedded error | +| | | | | | | | | | estimation is needed. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| GeneralShARK | Strat | space-time | 0.5/1.0 | 1.0/1.0 | 1.5/2.0 | 2 | 3 | No | General SDEs when embedded error | +| | | | | | | | | | estimaiton is not needed. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +| SlowRK | Strat | space-time | 0.5/1.0 | 1.5/2.0 | 1.5/2.0 | 2 | 5 | No | Commutative noise SDEs. | ++----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+ +``` \ No newline at end of file diff --git a/docs/devdocs/sde_example.ipynb b/docs/devdocs/sde_example.ipynb deleted file mode 100644 index 39253c9d..00000000 --- a/docs/devdocs/sde_example.ipynb +++ /dev/null @@ -1,709 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "initial_id", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:31:52.566803Z", - "start_time": "2024-07-31T18:31:52.563608Z" - }, - "collapsed": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: JAX_PLATFORM_NAME=cuda\n" - ] - } - ], - "source": [ - "%env JAX_PLATFORM_NAME=cuda\n", - "\n", - "from warnings import simplefilter\n", - "\n", - "\n", - "simplefilter(\"ignore\", category=FutureWarning)\n", - "\n", - "import diffrax\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "import matplotlib.pyplot as plt\n", - "from diffrax import (\n", - " SpaceTimeLevyArea,\n", - " SPaRK,\n", - ")\n", - "\n", - "\n", - "jax.config.update(\"jax_enable_x64\", True)\n", - "jnp.set_printoptions(precision=4, suppress=True)" - ] - }, - { - "cell_type": "markdown", - "id": "86d4e8b062a81d7e", - "metadata": {}, - "source": [ - "# Simulating SDEs in Diffrax\n", - "\n", - "We will be simulating a Stratonovich SDE of the form:\n", - "$$\n", - " dY_t = f(Y_t, t) dt + g(Y_t, t) \\circ dW_t, \n", - "$$\n", - "where $t \\in [0, T]$, $Y_t \\in \\mathbb{R}^e$, and $W$ is a standard Brownian motion on $\\mathbb{R}^d$. We refer to $f: \\mathbb{R}^e \\times [0, T] \\to \\mathbb{R}^e$ as the drift vector field and $g: \\mathbb{R}^e \\times [0, T] \\to \\mathbb{R}^{e \\times d}$ is the diffusion matrix field. The Stratonovich integral is denoted by $\\circ$.\n", - "\n", - "Our SDE will have the following drift and diffusion terms:\n", - "\\begin{align*}\n", - " f(Y_t, t) &= \\alpha - \\beta Y_t, \\\\\n", - " g(Y_t, t) &= \\gamma \\begin{bmatrix} \\Vert Y_t \\Vert_2 & 0 \\\\ 0 & Y_{t, 1} \\\\ 0 & 10t \\end{bmatrix},\n", - "\\end{align*}\n", - "where $\\alpha, \\gamma \\in \\mathbb{R}^3$ and $\\beta \\in \\mathbb{R}_{\\geq 0}$ are some parameters.\n", - "\n", - "Let's write the SDE in the form that Diffrax expects:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ba23e9cc0370fbac", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:31:54.176955Z", - "start_time": "2024-07-31T18:31:52.567678Z" - } - }, - "outputs": [], - "source": [ - "# Drift VF (e = 3)\n", - "def f(t, y, args):\n", - " alpha, beta, gamma = args\n", - " beta = jnp.abs(beta)\n", - " assert alpha.shape == (3,)\n", - " return jnp.array(alpha - beta * y, dtype=y.dtype)\n", - "\n", - "\n", - "# Diffusion matrix field (e = 3, d = 2)\n", - "def g(t, y, args):\n", - " alpha, beta, gamma = args\n", - " assert gamma.shape == y.shape == (3,)\n", - " gamma = jnp.reshape(gamma, (3, 1))\n", - " out = gamma * jnp.array(\n", - " [[jnp.sqrt(jnp.sum(y**2)), 0.0], [0.0, 3 * y[0]], [0.0, 20 * t]], dtype=y.dtype\n", - " )\n", - " return out\n", - "\n", - "\n", - "# Initial condition\n", - "y0 = jnp.array([1.0, 1.0, 1.0])\n", - "\n", - "# Args\n", - "alpha = 0.5 * jnp.ones((3,))\n", - "beta = 1.0\n", - "gamma = jnp.ones((3,))\n", - "args = (alpha, beta, gamma)\n", - "\n", - "# Time domain\n", - "t0 = 0.0\n", - "t1 = 2.0\n", - "dt0 = 2**-9" - ] - }, - { - "cell_type": "markdown", - "id": "ef2ff90865907b7d", - "metadata": {}, - "source": [ - "## Brownian motion and its Levy area\n", - "\n", - "Different solvers require different information about the Brownian motion. For example, the `SPaRK` solver requires access to the space-time Levy area of the Brownian motion. The required Levy area for each solver is documented in the table at the end of this notebook, or can be checked via `solver.minimal_levy_area`.\n", - " \n", - "We will use the `VirtualBrownianTree` class to generate the Brownian motion and its Levy area." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4110735158215acc", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:31:54.310082Z", - "start_time": "2024-07-31T18:31:54.178020Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Minimal levy area for SPaRK: AbstractSpaceTimeLevyArea.\n" - ] - } - ], - "source": [ - "# check minimal levy area\n", - "solver = SPaRK()\n", - "print(f\"Minimal levy area for SPaRK: {solver.minimal_levy_area.__name__}.\")\n", - "\n", - "# Brownian motion\n", - "key = jr.key(0)\n", - "bm_tol = 2**-13\n", - "bm_shape = (2,)\n", - "bm = diffrax.VirtualBrownianTree(\n", - " t0, t1, bm_tol, bm_shape, key, levy_area=SpaceTimeLevyArea\n", - ")\n", - "\n", - "# Defining the terms of the SDE\n", - "ode_term = diffrax.ODETerm(f)\n", - "diffusion_term = diffrax.ControlTerm(g, bm) # Note that the BM is baked into the term\n", - "terms = diffrax.MultiTerm(ode_term, diffusion_term)" - ] - }, - { - "cell_type": "markdown", - "id": "e71db03c5257bd46", - "metadata": {}, - "source": [ - "### Using `diffrax.diffeqsolve` to solve the SDE\n", - "\n", - "We will first use constant steps of size $h = 2^{-9}$ to solve the SDE. It is very important to have $h > \\mathtt{bm_tol}$, where $\\mathtt{bm_tol}$ is the tolerance of the Brownian motion.\n", - "\n", - " We will use the SPaRK method to solve the SDE. ShARK is a stochastic Runge-Kutta method that requires access to space-time Levy area." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8a969e1b9bd9f09", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:32:00.222743Z", - "start_time": "2024-07-31T18:31:54.310746Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sol = diffrax.diffeqsolve(\n", - " terms, SPaRK(), t0, t1, dt0, y0, args, saveat=diffrax.SaveAt(steps=True)\n", - ")\n", - "\n", - "# Plotting the solution on ax1 and the BM on ax2\n", - "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 8))\n", - "ax1.plot(sol.ts, sol.ys[:, 0], label=\"Y_1\")\n", - "ax1.plot(sol.ts, sol.ys[:, 1], label=\"Y_2\")\n", - "ax1.plot(sol.ts, sol.ys[:, 2], label=\"Y_3\")\n", - "ax1.set_title(\"SDE solution\")\n", - "ax1.legend()\n", - "\n", - "bm_vals = jax.vmap(lambda t: bm.evaluate(t0, t))(jnp.clip(sol.ts, t0, t1))\n", - "ax2.plot(sol.ts, bm_vals[:, 0], label=\"BM_1\")\n", - "ax2.plot(sol.ts, bm_vals[:, 1], label=\"BM_2\")\n", - "ax2.set_title(\"Brownian motion\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fd3251c814306cd", - "metadata": {}, - "source": [ - "## Using adaptive time-stepping via the PID-controller\n", - "\n", - "Note that the `SPaRK` solver has an embedded method for error estimation. For solvers like `GeneralShARK`, which do not have an embedded method, we'd instead need to use `HalfSolver(GeneralShARK())` as the solver." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "42ca5c5520079b5f", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:32:06.254299Z", - "start_time": "2024-07-31T18:32:00.223673Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accepted steps: 2462, Rejected steps: 1500, total steps: 3962\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "controller = diffrax.PIDController(\n", - " rtol=0,\n", - " atol=0.005,\n", - " pcoeff=0.2,\n", - " icoeff=0.5,\n", - " dcoeff=0,\n", - " dtmin=2**-12,\n", - " dtmax=0.25,\n", - ")\n", - "\n", - "solver = SPaRK()\n", - "# solver = diffrax.HalfSolver(diffrax.GeneralShARK())\n", - "\n", - "sol_pid_spark = diffrax.diffeqsolve(\n", - " terms,\n", - " solver,\n", - " t0,\n", - " t1,\n", - " dt0,\n", - " y0,\n", - " args,\n", - " saveat=diffrax.SaveAt(steps=True),\n", - " stepsize_controller=controller,\n", - " max_steps=2**16,\n", - ")\n", - "accepted_steps = sol_pid_spark.stats[\"num_accepted_steps\"]\n", - "rejected_steps = sol_pid_spark.stats[\"num_rejected_steps\"]\n", - "print(\n", - " f\"Accepted steps: {accepted_steps}, Rejected steps: {rejected_steps},\"\n", - " f\" total steps: {accepted_steps + rejected_steps}\"\n", - ")\n", - "\n", - "# Plot the solution on ax1 and the density of ts on ax2\n", - "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 8))\n", - "ax1.plot(sol_pid_spark.ts, sol_pid_spark.ys[:, 0], label=\"Y_1\")\n", - "ax1.plot(sol_pid_spark.ts, sol_pid_spark.ys[:, 1], label=\"Y_2\")\n", - "ax1.plot(sol_pid_spark.ts, sol_pid_spark.ys[:, 2], label=\"Y_3\")\n", - "ax1.set_title(\"SDE solution\")\n", - "ax1.legend()\n", - "\n", - "# Plot the density of ts\n", - "# sol_pid.ts is padded with inf values at the end, so we remove them\n", - "padding_idx = jnp.argmax(jnp.isinf(sol_pid_spark.ts))\n", - "ts = sol_pid_spark.ts[:padding_idx]\n", - "ax2.hist(ts, bins=100)\n", - "ax2.set_title(\"Density of ts\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "344b5f07d5120128", - "metadata": {}, - "source": [ - "## Solving an SDE for a batch of Brownian motions\n", - "\n", - "When doing Monte Carlo simulations, we often need to solve the same SDE for multiple Brownian motions. We can do this via `jax.vmap`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ffe3ced461ebb823", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:32:06.359136Z", - "start_time": "2024-07-31T18:32:06.255078Z" - } - }, - "outputs": [], - "source": [ - "def get_terms(bm):\n", - " return diffrax.MultiTerm(ode_term, diffrax.ControlTerm(g, bm))\n", - "\n", - "\n", - "# Fix which times we step to (this is equivalent to a constant step size)\n", - "# We do this because the combination of using dt0 and SaveAt(steps=True) pads the\n", - "# output with inf values up to max_steps.\n", - "# Instead we specify exactly which times we want to save at, so Diffrax allocates\n", - "# the correct amount of memory at the outset.\n", - "num_steps = int((t1 - t0) / dt0)\n", - "step_times = jnp.linspace(t0, t1, num_steps + 1, endpoint=True)\n", - "constant_controller = diffrax.StepTo(ts=step_times)\n", - "saveat = diffrax.SaveAt(ts=step_times)\n", - "\n", - "\n", - "# We will vmap over keys\n", - "@jax.jit\n", - "@jax.vmap\n", - "def batch_sde_solve(key):\n", - " bm = diffrax.VirtualBrownianTree(\n", - " t0, t1, bm_tol, bm_shape, key, levy_area=SpaceTimeLevyArea\n", - " )\n", - " terms = get_terms(bm)\n", - " return diffrax.diffeqsolve(\n", - " terms,\n", - " SPaRK(),\n", - " t0,\n", - " t1,\n", - " None,\n", - " y0,\n", - " args,\n", - " saveat=saveat,\n", - " stepsize_controller=constant_controller,\n", - " )\n", - "\n", - "\n", - "# Split the keys and compute the batched solutions\n", - "num_samples = 100\n", - "keys = jr.split(jr.PRNGKey(0), num_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3c1206025f30100d", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:32:10.954065Z", - "start_time": "2024-07-31T18:32:06.359825Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of batch_sols: (100, 1025, 3) == 100 x 1025 x (dim of Y)\n" - ] - } - ], - "source": [ - "batch_sols = batch_sde_solve(keys)\n", - "print(\n", - " f\"Shape of batch_sols: \"\n", - " f\"{batch_sols.ys.shape} == {num_samples} x {num_steps + 1} x (dim of Y)\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "71dda42d79d4c553", - "metadata": {}, - "source": [ - "## Optimizing wrt. SDE parameters\n", - "A function with a similar behaviour to `batch_sde_solve` is available in `test.helpers`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d278fc2d438ffc82", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:35:46.374632Z", - "start_time": "2024-07-31T18:33:46.960985Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of batch_ys: (100, 1025, 3) == 100 x 1025 x (dim of Y)\n", - "Stats at t=t1: mean=[ 6.3325 -1.8118 2.5538], var=[1025.4129 1988.8386 490.2592]\n", - "Step 0, loss: 54.37318141670484\n", - "Step 10, loss: 6.744215098806366\n", - "Step 20, loss: 2.998366856582221\n", - "Step 30, loss: 1.2659615064637102\n", - "Step 40, loss: 0.9366064334115017\n", - "Step 50, loss: 0.5297932248566202\n", - "Step 60, loss: 0.2899448067097704\n", - "Step 70, loss: 0.22884998150945443\n", - "Step 80, loss: 0.3022284048251223\n", - "Step 90, loss: 0.08371389038646312\n", - "Step 100, loss: 0.19500267926433498\n", - "Step 110, loss: 0.15579142632143467\n", - "Step 120, loss: 0.21476759365188788\n", - "Step 130, loss: 0.07189106656001293\n", - "Step 140, loss: 0.06617959592505693\n", - "Step 150, loss: 0.07004904780195191\n", - "Step 160, loss: 0.016133195408697235\n", - "Step 170, loss: 0.04618817294618282\n", - "Step 180, loss: 0.014085655094219525\n", - "Step 190, loss: 0.005163943744172692\n", - "Optimal parameters:\n", - "alpha=[ 0.667 3.4642 -0.7856], beta=3.3743923144904424, gamma=[-1.3289 -0.9053 0.1391]\n" - ] - } - ], - "source": [ - "from test.helpers import _batch_sde_solve\n", - "\n", - "import optax\n", - "from jax import Array\n", - "\n", - "\n", - "bm_shape = (2,)\n", - "# Note that _batch_sde_solve doesn't output the whole solution object but just a\n", - "# tuple (ys, num_steps_output). The number of steps is there for benchmarking.\n", - "batch_ys, num_steps_output = _batch_sde_solve(\n", - " keys,\n", - " get_terms,\n", - " bm_shape,\n", - " t0,\n", - " t1,\n", - " y0,\n", - " args,\n", - " SPaRK(),\n", - " SpaceTimeLevyArea,\n", - " None,\n", - " constant_controller,\n", - " bm_tol,\n", - " saveat,\n", - ")\n", - "\n", - "print(\n", - " f\"Shape of batch_ys: \"\n", - " f\"{batch_ys.shape} == {num_samples} x {num_steps + 1} x (dim of Y)\"\n", - ")\n", - "ys_t1 = batch_ys[:, -1]\n", - "mean_t1 = jnp.mean(ys_t1, axis=0)\n", - "var_t1 = jnp.mean(ys_t1**2, axis=0) - mean_t1**2\n", - "print(f\"Stats at t=t1: mean={mean_t1}, var={var_t1}\")\n", - "\n", - "\n", - "# We will optimize for achieving a mean of 0\n", - "def loss(args: tuple[Array, Array, Array]):\n", - " batch_ys, num_steps_output = _batch_sde_solve(\n", - " keys,\n", - " get_terms,\n", - " bm_shape,\n", - " t0,\n", - " t1,\n", - " y0,\n", - " args,\n", - " SPaRK(),\n", - " SpaceTimeLevyArea,\n", - " 2**-7,\n", - " None,\n", - " bm_tol,\n", - " diffrax.SaveAt(t1=True),\n", - " )\n", - " assert batch_ys.shape == (num_samples, 1, 3)\n", - " mean = jnp.mean(batch_ys, axis=(0, 1))\n", - " std = jnp.sqrt(jnp.mean(batch_ys**2, axis=(0, 1)) - mean**2)\n", - " target_mean = jnp.array([0.0, 1.0, 0.0])\n", - " target_stds = 2 * jnp.ones((3,))\n", - " loss = jnp.sqrt(\n", - " jnp.sum((mean - target_mean) ** 2) + jnp.sum((std - target_stds) ** 2)\n", - " )\n", - " return loss\n", - "\n", - "\n", - "# Define the parameters to optimize\n", - "alpha_opt = 0.5 * jnp.ones((3,))\n", - "beta_opt = jnp.array(1.0)\n", - "gamma_opt = jnp.ones((3,))\n", - "args_opt = (alpha_opt, beta_opt, gamma_opt)\n", - "\n", - "# Define the optimizer\n", - "num_steps = 191\n", - "schedule = optax.cosine_decay_schedule(3e-1, num_steps, 1e-2)\n", - "opt = optax.chain(\n", - " optax.scale_by_adam(b1=0.9, b2=0.99, eps=1e-8),\n", - " optax.scale_by_schedule(schedule),\n", - " optax.scale(-1),\n", - ")\n", - "# opt = optax.adam(2e-1)\n", - "opt_state = opt.init(args_opt)\n", - "\n", - "\n", - "@jax.jit\n", - "def step(i, opt_state, args):\n", - " loss_val, grad = jax.value_and_grad(loss)(args)\n", - " updates, opt_state = opt.update(grad, opt_state)\n", - "\n", - " # One way to apply updates\n", - " # args = optax.apply_updates(args, updates)\n", - "\n", - " # Another way to apply updates\n", - " args = jax.tree_util.tree_map(lambda x, u: x + u, args, updates)\n", - "\n", - " return opt_state, args, loss_val\n", - "\n", - "\n", - "for i in range(num_steps):\n", - " opt_state, args_opt, loss_val = step(i, opt_state, args_opt)\n", - " alpha_opt, beta_opt, gamma_opt = args_opt\n", - " if i % 10 == 0:\n", - " print(f\"Step {i}, loss: {loss_val}\")\n", - "\n", - "print(\n", - " f\"Optimal parameters:\\n\"\n", - " f\"alpha={alpha_opt},\"\n", - " f\" beta={beta_opt}, gamma={gamma_opt}\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "834651877787c7e6", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-31T18:35:50.989098Z", - "start_time": "2024-07-31T18:35:46.375499Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stats at t=t1: mean=[-0.0002 1.0011 -0.0003], var=[4.1182 4.1616 3.9834]\n" - ] - } - ], - "source": [ - "batch_ys_opt, num_steps_output = _batch_sde_solve(\n", - " keys,\n", - " get_terms,\n", - " bm_shape,\n", - " t0,\n", - " t1,\n", - " y0,\n", - " args_opt,\n", - " SPaRK(),\n", - " SpaceTimeLevyArea,\n", - " None,\n", - " constant_controller,\n", - " bm_tol,\n", - " saveat,\n", - ")\n", - "ys_t1 = batch_ys_opt[:, -1]\n", - "mean_t1 = jnp.mean(ys_t1, axis=0)\n", - "var_t1 = jnp.mean(ys_t1**2, axis=0) - mean_t1**2\n", - "print(f\"Stats at t=t1: mean={mean_t1}, var={var_t1}\")" - ] - }, - { - "cell_type": "markdown", - "id": "fa031b6ebb679028", - "metadata": {}, - "source": [ - "# Table of available SRK methods in Diffrax\n", - "\n", - "## Itô vs Stratonovich SDEs\n", - "Some of the solvers converge to the Itô solution of the SDE and others to the Stratonovich solution. The Itô and Stratonovich solutions coincide iff the SDE has additive noise (as defined below). For other SDEs it is possible to convert them between the Itô and Stratonovich versions using the Itô-Stratonovich correction term.\n", - "\n", - "\n", - "## Noise type\n", - "Depending on the type of noise (i.e. diffusion term) present in the SDE, different SRK methods have different strong orders of convergence. These types of noise are the same for Itô and Stratonovich SDEs.\n", - "\n", - "\n", - "### General noise\n", - "Any SDE of the form (as above):\n", - "$$\n", - " dY_t = f(Y_t, t) dt + g(Y_t, t) dW_t, \n", - "$$\n", - "where $t \\in [0, T]$, $Y_t \\in \\mathbb{R}^e$, and $W$ is a standard Brownian motion on $\\mathbb{R}^d$. We refer to $f: \\mathbb{R}^e \\times [0, T] \\to \\mathbb{R}^e$ as the drift vector field and $g: \\mathbb{R}^e \\times [0, T] \\to \\mathbb{R}^{e \\times d}$ is the diffusion matrix field with columns $g_i$ for $i = 1, \\ldots, d$.\n", - "\n", - "\n", - "### Commutative noise\n", - "We say that the diffusion is commutative when the columns of $g$ commute in the Lie bracket, that is\n", - "$$\n", - " \\frac{d}{dy} g_i(y, t) \\; g_j(y, t) = g_i(y, t) \\, \\frac{d}{dy} g_j(y, t) \\quad \\forall \\, y, t.\n", - "$$\n", - "For example, this holds when $g$ is diagonal or when the dimension of BM is $d=1$.\n", - "\n", - "\n", - "### Additive noise\n", - "We say that the diffusion is additive when $g$ does not depend on $Y_t$ and the SDE can be written as\n", - "$$\n", - " dY_t = f(Y_t, t) dt + g(t) dW_t.\n", - "$$\n", - "Additive noise is a special case of commutative noise. For additive noise SDEs, the Itô and Stratonovich solutions conicide. Some solvers (ShARK, SRA1, SEA) are specifically designed for additive noise SDEs, so for those we do not specify whether they are Itô or Stratonovich." - ] - }, - { - "cell_type": "markdown", - "id": "4a49d8a22ccfcfe9", - "metadata": {}, - "source": [ - "```\n", - "+----------------+-------+------------+------------------------------------+-------------------+----------------+------------------------------------------+\n", - "| | SDE | Lévy | Strong/weak order (per noise type) | VF evaluations | Embedded error | Recommended for |\n", - "| | type | area +----------+--------------+----------+-------+-----------+ estimation | (and other notes) |\n", - "| | | | General | Commutative | Additive | Drift | Diffusion | | |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| Euler | Itô | BM only | 0.5/1.0 | 0.5/1.0 | 1.0/1.0 | 1 | 1 | No | Itô SDEs when a cheap solver is needed. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| Heun | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | Standard solver for Stratonovich SDEs. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| EulerHeun | Strat | BM only | 0.5/1.0 | 0.5/1.0 | 1.0/1.0 | 1 | 2 | No | Stratonovich SDEs with expensive drift. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| ItoMilstein | Itô | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 1 | 1 | No | Better than Euler for Itô SDEs, but |\n", - "| | | | | | | | | | comuptes the derivative of diffusion VF. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| Stratonovich | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 1 | 1 | No | For commutative Stratonovich SDEs when |\n", - "| Milstein | | | | | | | | | space-time Lévy area is not available. |\n", - "| | | | | | | | | | Computes derivative of diffusion VF. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| ReversibleHeun | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | When a reversible solver is needed. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| Midpoint | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | Usually Heun should be preferred. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| Ralston | Strat | BM only | 0.5/1.0 | 1.0/1.0 | 1.0/1.0 | 2 | 2 | Yes | Usually Heun should be preferred. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| ShARK | Strat | space-time | / | / | 1.5/2.0 | 2 | 2 | Yes | Additive noise SDEs. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| SRA1 | Strat | space-time | / | / | 1.5/2.0 | 2 | 2 | Yes | Only slightly worse than ShARK. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| SEA | Strat | space-time | / | / | 1.0/1.0 | 1 | 1 | No | Cheap solver for additive noise SDEs. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| SPaRK | Strat | space-time | 0.5/1.0 | 1.0/1.0 | 1.5/2.0 | 3 | 3 | Yes | General SDEs when embedded error |\n", - "| | | | | | | | | | estimation is needed. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| GeneralShARK | Strat | space-time | 0.5/1.0 | 1.0/1.0 | 1.5/2.0 | 2 | 3 | No | General SDEs when embedded error |\n", - "| | | | | | | | | | estimaiton is not needed. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "| SlowRK | Strat | space-time | 0.5/1.0 | 1.5/2.0 | 1.5/2.0 | 2 | 5 | No | Commutative noise SDEs. |\n", - "+----------------+-------+------------+----------+--------------+----------+-------+-----------+----------------+------------------------------------------+\n", - "```" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/usage/how-to-choose-a-solver.md b/docs/usage/how-to-choose-a-solver.md index 669ef2e9..0cb719a8 100644 --- a/docs/usage/how-to-choose-a-solver.md +++ b/docs/usage/how-to-choose-a-solver.md @@ -42,54 +42,51 @@ For "split stiffness" problems, with one term that is stiff and another term tha ## Stochastic differential equations -SDE solvers are relatively specialised depending on the type of problem. Each solver will converge to either the Itô solution or the Stratonovich solution. In addition some solvers require "commutative noise". +SDE solvers are relatively specialised depending on the type of problem. Each solver will converge to either the Itô solution or the Stratonovich solution of the SDE. The Itô and Stratonovich solutions coincide iff the SDE has additive noise (as defined below). In addition some solvers require the SDE to have "commutative noise" or "additive noise". All of these terms are defined below. -!!! info "Commutative noise" +### General (noncommutative) noise +This includes any SDE of the form $dy(t) = f(y(t), t) dt + g(y(t), t) dw(t),$ where $t \in [0, T]$, $y(t) \in \mathbb{R}^e$, and $w$ is a standard Brownian motion on $\mathbb{R}^d$. We refer to $f: \mathbb{R}^e \times [0, T] \to \mathbb{R}^e$ as the drift vector field (VF) and $g: \mathbb{R}^e \times [0, T] \to \mathbb{R}^{e \times d}$ is the diffusion matrix field with columns $g_i$ for $i = 1, \ldots, d$. - Consider the SDE - $\mathrm{d}y(t) = μ(t, y(t))\mathrm{d}t + σ(t, y(t))\mathrm{d}w(t)$ +### Commutative noise +The diffusion matrix $σ$ is said to satisfy the commutativity condition if - then the diffusion matrix $σ$ is said to satisfy the commutativity condition if +$\sum_{i=1}^d g_{i, j} \frac{\partial g_{k, l}}{\partial y_i} = \sum_{i=1}^d g_{i, l} \frac{\partial g_{k, j}}{\partial y_i}$ - $\sum_{i=1}^d σ_{i, j} \frac{\partial σ_{k, l}}{\partial y_i} = \sum_{i=1}^d σ_{i, l} \frac{\partial σ_{k, j}}{\partial y_i}$ +For example, this holds: - Some common special cases in which this condition is satisfied are: +- when $g$ is a diagonal operator (i.e. $g(y,t)$ is a diagonal matrix for all $y, t$ and the i-th diagonal entry depends only on $y_i$), +- when the dimension of BM is $d=1$, or +- when the noise is additive (see below). - - the diffusion is additive ($σ$ is independent of $y$); - - the noise is scalar ($w$ is scalar-valued); - - the diffusion is diagonal ($σ$ is a diagonal matrix and such that the i-th - diagonal entry depends only on $y_i$; *not* to be confused with the simpler - but insufficient condition that $σ$ is only a diagonal matrix) +- The solver with the highest order of convergence for commutative noise SDEs is [`diffrax.SlowRK`][]. [`diffrax.ItoMilstein`][] and [`diffrax.StratonovichMilstein`][] are alternatives which evaluate the vector field fewer times per step, but also compute its derivative. + + +### Additive noise +We say that the diffusion is additive when $g$ does not depend on $y(t)$ and the SDE can be written as $dy(t) = f(y(t), t) dt + g(t) dw(t)$. + +Additive noise is a special case of commutative noise. For additive noise SDEs, the Itô and Stratonovich solutions conicide. Some solvers are specifically designed for additive noise SDEs, of these [`diffrax.SEA`][] is the cheapest, [`diffrax.ShARK`][] is the most accurate and [`diffrax.SRA1`][] is another alternative. ### Itô For Itô SDEs: +- For general noise [`diffrax.Euler`][] is a typical choice. - If the noise is commutative then [`diffrax.ItoMilstein`][] is a typical choice; -- If the noise is noncommutative then [`diffrax.Euler`][] is a typical choice. ### Stratonovich For Stratonovich SDEs: - If cheap low-accuracy solves are desired then [`diffrax.EulerHeun`][] is a typical choice. -- Otherwise, and if the noise is commutative, then [`diffrax.SlowRK`][] has the best order of convergence, but is expensive per step. [`diffrax.StratonovichMilstein`][] is a good cheap alternative. -- If the noise is noncommutative, [`diffrax.GeneralShARK`][] is the most efficient choice, while [`diffrax.Heun`][] is a good cheap alternative. -- If the noise is noncommutative and an embedded method for adaptive step size control is desired, then [`diffrax.SPaRK`][] is the recommended choice. - -### Additive noise - -Consider the SDE - -$\mathrm{d}y(t) = μ(t, y(t))\mathrm{d}t + σ(t, y(t))\mathrm{d}w(t)$ - -Then the diffusion matrix $σ$ is said to be additive if $σ(t, y) = σ(t)$. That is to say if the diffusion is independent of $y$. +- For general noise, [`diffrax.GeneralShARK`][] is the most efficient choice, while [`diffrax.Heun`][] is a good cheap alternative. +- If an embedded method for adaptive step size control is desired and the noise is noncommutative then [`diffrax.SPaRK`][] is the recommended choice. +- If the noise is commutative, then [`diffrax.SlowRK`][] has the best order of convergence, but is expensive per step. [`diffrax.StratonovichMilstein`][] is a good cheaper alternative. -In this case the Itô solution and the Stratonovich solution coincide, and mathematically speaking the choice of Itô vs Stratonovich is unimportant. Special solvers for additive-noise SDEs tend to do particularly well as compared to the general Itô or Stratonovich solvers discussed above. +### More information about SDE solvers -- The cheapest (but least accurate) solver is [`diffrax.SEA`][]. -- Otherwise [`diffrax.ShARK`][] or [`diffrax.SRA1`][] are good choices. +A detailed example of how to simulate SDEs can be found in the [SDE example](../examples/sde_example.ipynb). +A table of all SDE solvers and their properties can be found in [SDE solver table](../devdocs/SDE_solver_table.md). --- diff --git a/examples/sde_example.ipynb b/examples/sde_example.ipynb new file mode 100644 index 00000000..3cf9c490 --- /dev/null +++ b/examples/sde_example.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:03:24.933326Z", + "start_time": "2024-08-07T15:03:24.930405Z" + }, + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: JAX_PLATFORM_NAME=cuda\n" + ] + } + ], + "source": [ + "%env JAX_PLATFORM_NAME=cuda\n", + "\n", + "from warnings import simplefilter\n", + "\n", + "\n", + "simplefilter(\"ignore\", category=FutureWarning)\n", + "\n", + "from functools import partial\n", + "\n", + "import diffrax\n", + "import equinox as eqx\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "import matplotlib.pyplot as plt\n", + "import optax\n", + "from jaxtyping import Array\n", + "\n", + "\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "jnp.set_printoptions(precision=4, suppress=True)" + ] + }, + { + "cell_type": "markdown", + "id": "86d4e8b062a81d7e", + "metadata": {}, + "source": [ + "# Advanced SDE example\n", + "\n", + "We will be simulating a Stratonovich SDE of the form:\n", + "\n", + "$$\n", + " dy(t) = f(y(t), t) dt + g(y(t), t) \\circ dw(t), \n", + "$$\n", + "\n", + "where $t \\in [0, T]$, $y(t) \\in \\mathbb{R}^e$, and $w$ is a standard Brownian motion on $\\mathbb{R}^d$. We refer to $f: \\mathbb{R}^e \\times [0, T] \\to \\mathbb{R}^e$ as the drift vector field and $g: \\mathbb{R}^e \\times [0, T] \\to \\mathbb{R}^{e \\times d}$ is the diffusion matrix field. The Stratonovich integral is denoted by $\\circ$.\n", + "\n", + "Our SDE will have the following drift and diffusion terms:\n", + "\n", + "\\begin{align*}\n", + " f(y(t), t) &= \\alpha - \\beta y(t), \\\\\n", + " g(y(t), t) &= \\gamma \\begin{bmatrix} \\Vert y(t) \\Vert_2 & 0 \\\\ 0 & y_1(t) \\\\ 0 & 10t \\end{bmatrix},\n", + "\\end{align*}\n", + "\n", + "where $\\alpha, \\gamma \\in \\mathbb{R}^3$ and $\\beta \\in \\mathbb{R}_{\\geq 0}$ are some parameters.\n", + "\n", + "Let's write the SDE in the form that Diffrax expects:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba23e9cc0370fbac", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:00:57.326485Z", + "start_time": "2024-08-07T15:00:57.114638Z" + } + }, + "outputs": [], + "source": [ + "# Drift VF (e = 3)\n", + "def f(t, y, args):\n", + " alpha, beta, gamma = args\n", + " beta = jnp.abs(beta)\n", + " assert alpha.shape == (3,)\n", + " return jnp.array(alpha - beta * y, dtype=y.dtype)\n", + "\n", + "\n", + "# Diffusion matrix field (e = 3, d = 2)\n", + "def g(t, y, args):\n", + " alpha, beta, gamma = args\n", + " assert gamma.shape == y.shape == (3,)\n", + " gamma = jnp.reshape(gamma, (3, 1))\n", + " out = gamma * jnp.array(\n", + " [[jnp.sqrt(jnp.sum(y**2)), 0.0], [0.0, 3 * y[0]], [0.0, 20 * t]], dtype=y.dtype\n", + " )\n", + " return out\n", + "\n", + "\n", + "# Initial condition\n", + "y0 = jnp.array([1.0, 1.0, 1.0])\n", + "\n", + "# Args\n", + "alpha = 0.5 * jnp.ones((3,))\n", + "beta = 1.0\n", + "gamma = jnp.ones((3,))\n", + "args = (alpha, beta, gamma)\n", + "\n", + "# Time domain\n", + "t0 = 0.0\n", + "t1 = 2.0\n", + "dt0 = 2**-9" + ] + }, + { + "cell_type": "markdown", + "id": "ef2ff90865907b7d", + "metadata": {}, + "source": [ + "## Brownian motion and its Levy area\n", + "\n", + "Different solvers require different information about the Brownian motion. For example, the `SPaRK` solver requires access to the space-time Levy area of the Brownian motion. The required Levy area for each solver is documented in the table at the end of this notebook, or can be checked via `solver.minimal_levy_area`.\n", + " \n", + "We will use the `VirtualBrownianTree` class to generate the Brownian motion and its Levy area." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4110735158215acc", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:00:57.436685Z", + "start_time": "2024-08-07T15:00:57.327093Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimal levy area for SPaRK: .\n" + ] + } + ], + "source": [ + "# check minimal levy area\n", + "solver = diffrax.SPaRK()\n", + "print(f\"Minimal levy area for SPaRK: {solver.minimal_levy_area}.\")\n", + "\n", + "# Brownian motion\n", + "key = jr.key(0)\n", + "bm_tol = 2**-13\n", + "bm_shape = (2,)\n", + "bm = diffrax.VirtualBrownianTree(\n", + " t0, t1, bm_tol, bm_shape, key, levy_area=diffrax.SpaceTimeLevyArea\n", + ")\n", + "\n", + "# Defining the terms of the SDE\n", + "ode_term = diffrax.ODETerm(f)\n", + "diffusion_term = diffrax.ControlTerm(g, bm) # Note that the BM is baked into the term\n", + "terms = diffrax.MultiTerm(ode_term, diffusion_term)" + ] + }, + { + "cell_type": "markdown", + "id": "e71db03c5257bd46", + "metadata": {}, + "source": [ + "### Using `diffrax.diffeqsolve` to solve the SDE\n", + "\n", + "We will first use constant steps of size $h = 2^{-9}$ to solve the SDE. It is very important to have $h > \\mathtt{bm\\_tol}$, where $\\mathtt{bm\\_tol}$ is the tolerance of the Brownian motion. This is important because the output distribution of the VirtualBrownianTree is precise as long as the times that we sample it at are at least $\\mathtt{bm\\_tol}$ apart. For more details see the [Single-seed Brownian Motion paper](https://arxiv.org/abs/2405.06464).\n", + "\n", + " We will use the SPaRK solver to solve the SDE. SPaRK is a stochastic Runge-Kutta method that requires access to space-time Levy area." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8a969e1b9bd9f09", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:01:03.639891Z", + "start_time": "2024-08-07T15:00:57.437552Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = diffrax.diffeqsolve(\n", + " terms, diffrax.SPaRK(), t0, t1, dt0, y0, args, saveat=diffrax.SaveAt(steps=True)\n", + ")\n", + "\n", + "# Plotting the solution on ax1 and the BM on ax2\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 8))\n", + "ax1.plot(sol.ts, sol.ys[:, 0], label=\"y_1\")\n", + "ax1.plot(sol.ts, sol.ys[:, 1], label=\"y_2\")\n", + "ax1.plot(sol.ts, sol.ys[:, 2], label=\"y_3\")\n", + "ax1.set_title(\"SDE solution\")\n", + "ax1.legend()\n", + "\n", + "bm_vals = jax.vmap(lambda t: bm.evaluate(t0, t))(jnp.clip(sol.ts, t0, t1))\n", + "ax2.plot(sol.ts, bm_vals[:, 0], label=\"BM_1\")\n", + "ax2.plot(sol.ts, bm_vals[:, 1], label=\"BM_2\")\n", + "ax2.set_title(\"Brownian motion\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fd3251c814306cd", + "metadata": {}, + "source": [ + "## Using adaptive time-stepping via the PID-controller\n", + "\n", + "In order to use adaptive time stepping, the solver must produce an estimate of its error on each step. This is then used by the PID controller to adjust the step size.\n", + "To perform this error estimation the `SPaRK` solver uses an embedded method. For solvers like `GeneralShARK`, which do not have an embedded method, we'd instead need to use `HalfSolver(GeneralShARK())` as the solver in order to estimate the error." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "42ca5c5520079b5f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:01:09.676011Z", + "start_time": "2024-08-07T15:01:03.640695Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accepted steps: 2462, Rejected steps: 1500, total steps: 3962\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controller = diffrax.PIDController(\n", + " rtol=0,\n", + " atol=0.005,\n", + " pcoeff=0.2,\n", + " icoeff=0.5,\n", + " dcoeff=0,\n", + " dtmin=2**-12,\n", + " dtmax=0.25,\n", + ")\n", + "\n", + "solver = diffrax.SPaRK()\n", + "# solver = diffrax.HalfSolver(diffrax.GeneralShARK())\n", + "\n", + "sol_pid_spark = diffrax.diffeqsolve(\n", + " terms,\n", + " solver,\n", + " t0,\n", + " t1,\n", + " dt0,\n", + " y0,\n", + " args,\n", + " saveat=diffrax.SaveAt(steps=True),\n", + " stepsize_controller=controller,\n", + " max_steps=2**16,\n", + ")\n", + "accepted_steps = sol_pid_spark.stats[\"num_accepted_steps\"]\n", + "rejected_steps = sol_pid_spark.stats[\"num_rejected_steps\"]\n", + "print(\n", + " f\"Accepted steps: {accepted_steps}, Rejected steps: {rejected_steps},\"\n", + " f\" total steps: {accepted_steps + rejected_steps}\"\n", + ")\n", + "\n", + "# Plot the solution on ax1 and the density of ts on ax2\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 8))\n", + "ax1.plot(sol_pid_spark.ts, sol_pid_spark.ys[:, 0], label=\"y_1\")\n", + "ax1.plot(sol_pid_spark.ts, sol_pid_spark.ys[:, 1], label=\"y_2\")\n", + "ax1.plot(sol_pid_spark.ts, sol_pid_spark.ys[:, 2], label=\"y_3\")\n", + "ax1.set_title(\"SDE solution\")\n", + "ax1.legend()\n", + "\n", + "# Plot the density of ts\n", + "# sol_pid.ts is padded with inf values at the end, so we remove them\n", + "padding_idx = jnp.argmax(jnp.isinf(sol_pid_spark.ts))\n", + "ts = sol_pid_spark.ts[:padding_idx]\n", + "ax2.hist(ts, bins=100)\n", + "ax2.set_title(\"Density of ts\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "344b5f07d5120128", + "metadata": {}, + "source": [ + "## Solving an SDE for a batch of Brownian motions\n", + "\n", + "When doing Monte Carlo simulations, we often need to solve the same SDE for multiple Brownian motions. We can do this via `jax.vmap`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ffe3ced461ebb823", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:08:08.495409Z", + "start_time": "2024-08-07T15:08:08.472527Z" + } + }, + "outputs": [], + "source": [ + "def get_terms(bm):\n", + " return diffrax.MultiTerm(ode_term, diffrax.ControlTerm(g, bm))\n", + "\n", + "\n", + "# Fix which times we step to (this is equivalent to a constant step size)\n", + "# We do this because the combination of using dt0 and SaveAt(steps=True) pads the\n", + "# output with inf values up to max_steps.\n", + "# Instead we specify exactly which times we want to save at, so Diffrax allocates\n", + "# the correct amount of memory at the outset.\n", + "num_steps = 2**8\n", + "step_times = jnp.linspace(t0, t1, num_steps + 1, endpoint=True)\n", + "constant_controller = diffrax.StepTo(ts=step_times)\n", + "saveat = diffrax.SaveAt(ts=step_times)\n", + "\n", + "\n", + "# We will vmap over keys\n", + "@eqx.filter_jit\n", + "@partial(jax.vmap, in_axes=(0, None, None))\n", + "def batch_sde_solve(key, saveat, args):\n", + " bm = diffrax.VirtualBrownianTree(\n", + " t0, t1, bm_tol, bm_shape, key, levy_area=diffrax.SpaceTimeLevyArea\n", + " )\n", + " terms = get_terms(bm)\n", + " return diffrax.diffeqsolve(\n", + " terms,\n", + " diffrax.SPaRK(),\n", + " t0,\n", + " t1,\n", + " None,\n", + " y0,\n", + " args,\n", + " saveat=saveat,\n", + " stepsize_controller=constant_controller,\n", + " )\n", + "\n", + "\n", + "# Split the keys and compute the batched solutions\n", + "num_samples = 100\n", + "keys = jr.split(jr.PRNGKey(0), num_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3c1206025f30100d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:08:12.884814Z", + "start_time": "2024-08-07T15:08:09.184768Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of batch_sols: (100, 257, 3) == 100 x 257 x (dim of y)\n" + ] + } + ], + "source": [ + "batch_sols = batch_sde_solve(keys, saveat, args)\n", + "print(\n", + " f\"Shape of batch_sols: \"\n", + " f\"{batch_sols.ys.shape} == {num_samples} x {num_steps + 1} x (dim of y)\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "71dda42d79d4c553", + "metadata": {}, + "source": [ + "## Optimizing wrt. SDE parameters\n", + "We will optimize the SDE parameters with the aim of achieving a mean of 0 and variance 4 at time `t1`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d278fc2d438ffc82", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:10:46.294696Z", + "start_time": "2024-08-07T15:08:14.303121Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 1, 3)\n", + "Stats at t=t1: mean=[ 6.2142 -1.858 2.5538], var=[ 968.0161 1816.357 490.2578]\n", + "Step 0, loss: 54.37318141670484\n", + "Step 10, loss: 6.744215098806366\n", + "Step 20, loss: 2.998366856582221\n", + "Step 30, loss: 1.2659615064637102\n", + "Step 40, loss: 0.9366064334115017\n", + "Step 50, loss: 0.5297932248566202\n", + "Step 60, loss: 0.2899448067097704\n", + "Step 70, loss: 0.22884998150945443\n", + "Step 80, loss: 0.3022284048251223\n", + "Step 90, loss: 0.08371389038646312\n", + "Step 100, loss: 0.19500267926433498\n", + "Step 110, loss: 0.15579142632143467\n", + "Step 120, loss: 0.21476759365188788\n", + "Step 130, loss: 0.07189106656001293\n", + "Step 140, loss: 0.06617959592505693\n", + "Step 150, loss: 0.07004904780195191\n", + "Step 160, loss: 0.016133195408697235\n", + "Step 170, loss: 0.04618817294618282\n", + "Step 180, loss: 0.014085655094219525\n", + "Step 190, loss: 0.005163943744172692\n", + "Optimal parameters:\n", + "alpha=[ 0.667 3.4642 -0.7856], beta=3.3743923144904424, gamma=[-1.3289 -0.9053 0.1391]\n" + ] + } + ], + "source": [ + "saveat_t1 = diffrax.SaveAt(t1=True)\n", + "batch_ys = batch_sde_solve(keys, saveat_t1, args).ys\n", + "print(batch_ys.shape)\n", + "ys_t1 = batch_ys[:, 0]\n", + "mean_t1 = jnp.mean(ys_t1, axis=0)\n", + "var_t1 = jnp.mean(ys_t1**2, axis=0) - mean_t1**2\n", + "print(f\"Stats at t=t1: mean={mean_t1}, var={var_t1}\")\n", + "\n", + "\n", + "# We will optimize for achieving a mean of 0\n", + "def loss(args: tuple[Array, Array, Array]):\n", + " _batch_sols = batch_sde_solve(keys, saveat_t1, args)\n", + " batch_ys = _batch_sols.ys\n", + " assert batch_ys.shape == (num_samples, 1, 3)\n", + " mean = jnp.mean(batch_ys, axis=(0, 1))\n", + " std = jnp.sqrt(jnp.mean(batch_ys**2, axis=(0, 1)) - mean**2)\n", + " target_mean = jnp.array([0.0, 1.0, 0.0])\n", + " target_stds = 2 * jnp.ones((3,))\n", + " loss = jnp.sqrt(\n", + " jnp.sum((mean - target_mean) ** 2) + jnp.sum((std - target_stds) ** 2)\n", + " )\n", + " return loss\n", + "\n", + "\n", + "# Define the parameters to optimize\n", + "alpha_opt = 0.5 * jnp.ones((3,))\n", + "beta_opt = jnp.array(1.0)\n", + "gamma_opt = jnp.ones((3,))\n", + "args_opt = (alpha_opt, beta_opt, gamma_opt)\n", + "\n", + "# Define the optimizer\n", + "num_steps = 191\n", + "schedule = optax.cosine_decay_schedule(3e-1, num_steps, 1e-2)\n", + "opt = optax.chain(\n", + " optax.scale_by_adam(b1=0.9, b2=0.99, eps=1e-8),\n", + " optax.scale_by_schedule(schedule),\n", + " optax.scale(-1),\n", + ")\n", + "# opt = optax.adam(2e-1)\n", + "opt_state = opt.init(args_opt)\n", + "\n", + "\n", + "@jax.jit\n", + "def step(i, opt_state, args):\n", + " loss_val, grad = jax.value_and_grad(loss)(args)\n", + " updates, opt_state = opt.update(grad, opt_state)\n", + "\n", + " # One way to apply updates\n", + " # args = optax.apply_updates(args, updates)\n", + "\n", + " # Another way to apply updates\n", + " args = jax.tree_util.tree_map(lambda x, u: x + u, args, updates)\n", + "\n", + " return opt_state, args, loss_val\n", + "\n", + "\n", + "for i in range(num_steps):\n", + " opt_state, args_opt, loss_val = step(i, opt_state, args_opt)\n", + " alpha_opt, beta_opt, gamma_opt = args_opt\n", + " if i % 10 == 0:\n", + " print(f\"Step {i}, loss: {loss_val}\")\n", + "\n", + "print(\n", + " f\"Optimal parameters:\\n\"\n", + " f\"alpha={alpha_opt},\"\n", + " f\" beta={beta_opt}, gamma={gamma_opt}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "834651877787c7e6", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-07T15:11:09.223494Z", + "start_time": "2024-08-07T15:11:05.397276Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stats at t=t1: mean=[ 0.0007 0.9999 -0.0003], var=[3.9827 3.9803 3.9832]\n" + ] + } + ], + "source": [ + "batch_ys_opt = batch_sde_solve(keys, saveat_t1, args_opt).ys\n", + "ys_t1 = batch_ys_opt[:, -1]\n", + "mean_t1 = jnp.mean(ys_t1, axis=0)\n", + "var_t1 = jnp.mean(ys_t1**2, axis=0) - mean_t1**2\n", + "print(f\"Stats at t=t1: mean={mean_t1}, var={var_t1}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d103fe1695cdd847", + "metadata": {}, + "source": "With the magic of JAX and Diffrax we were able to differentiate through the SDE solver and optimize the parameters of the SDE to achieve the desired mean and variance at time `t1`." + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mkdocs.yml b/mkdocs.yml index 6b4c2547..9be55771 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -112,6 +112,7 @@ nav: - Kalman filter: 'examples/kalman_filter.ipynb' - Second-order sensitivities: 'examples/hessian.ipynb' - Nonlinear heat PDE: 'examples/nonlinear_heat_pde.ipynb' + - Advanced SDE simulation example: 'examples/sde_example.ipynb' - Basic API: - 'api/diffeqsolve.md' - Solvers: @@ -139,3 +140,4 @@ nav: - 'devdocs/predictor_dirk.md' - 'devdocs/adjoint_commutative_noise.md' - Stochastic Runge-Kutta methods: 'devdocs/srk_example.ipynb' + - Table of SDE solvers: 'devdocs/SDE_solver_table.md'