diff --git a/notebooks/22_dc-resistivity-inversion-w-bfgs-precond.ipynb b/notebooks/22_dc-resistivity-inversion-w-bfgs-precond.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "af3e2f3b-e1fd-4ea9-b3c2-26bbc6308c2c",
+   "metadata": {},
+   "source": [
+    "# 2.5 DC Resisitivy inversion using BFGS as preconditioner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0888671c-f9b1-4b4e-8eb2-f8aca720920e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:39.722520Z",
+     "iopub.status.busy": "2026-04-13T23:24:39.722254Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.410672Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.409898Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:39.722492Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "\n",
+    "import discretize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pooch\n",
+    "import simpeg\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from simpeg.electromagnetics.static import resistivity as dc\n",
+    "\n",
+    "import inversion_ideas as ii"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2647ceb4-5d51-4c15-89a8-0049f35d603c",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0b8dce76-9a8e-42f2-8ddf-f3dcae0674a5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.411332Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.411032Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.418044Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.417403Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.411313Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[PosixPath('/home/santi/.cache/pooch/a567d6514bf50ed948b5d7b7e4125664-inv_dcr_2d_files.tar.gz.untar/inv_dcr_2d_files/topo_2d.txt'),\n",
+       " PosixPath('/home/santi/.cache/pooch/a567d6514bf50ed948b5d7b7e4125664-inv_dcr_2d_files.tar.gz.untar/inv_dcr_2d_files/dc_data.obs')]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = \"https://github.com/simpeg/user-tutorials/raw/main/assets/05-dcr/inv_dcr_2d_files.tar.gz\"\n",
+    "\n",
+    "fnames = pooch.retrieve(url, known_hash=None, processor=pooch.Untar())\n",
+    "fnames = [pathlib.Path(f) for f in fnames]\n",
+    "fnames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8db572c3-2960-42ca-9d52-0547431c5a80",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.418945Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.418637Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.438375Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.437546Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.418916Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(PosixPath('/home/santi/.cache/pooch/a567d6514bf50ed948b5d7b7e4125664-inv_dcr_2d_files.tar.gz.untar/inv_dcr_2d_files/topo_2d.txt'),\n",
+       " PosixPath('/home/santi/.cache/pooch/a567d6514bf50ed948b5d7b7e4125664-inv_dcr_2d_files.tar.gz.untar/inv_dcr_2d_files/dc_data.obs'))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(topo_fname,) = [f for f in fnames if \"topo_2d.txt\" in f.name]\n",
+    "(data_fname,) = [f for f in fnames if \"dc_data.obs\" in f.name]\n",
+    "topo_fname, data_fname"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "14621398-5fe7-4fe2-843d-7d067e026e18",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.439259Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.439037Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.449050Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.448077Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.439235Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load 2D topography\n",
+    "topo_2d = np.loadtxt(str(topo_fname))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d994d154-91bb-4fb2-82ce-e3dc7e9f66cf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.450181Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.449915Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.467356Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.466574Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.450155Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "voltage_data = simpeg.utils.io_utils.read_dcip2d_ubc(data_fname, \"volt\", \"general\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0dc7c54b-8df0-42ac-8ab9-01302ffb7b59",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.468139Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.467896Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.471634Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.470699Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.468114Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Apply uncertainties to normalized voltage data\n",
+    "voltage_data.standard_deviation = 1e-7 + 0.05 * np.abs(voltage_data.dobs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "79ca22c8-4258-400f-8bf6-01b4f0c63641",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.472740Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.472386Z",
+     "iopub.status.idle": "2026-04-13T23:24:41.480420Z",
+     "shell.execute_reply": "2026-04-13T23:24:41.479565Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.472712Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Get apparent conductivities from volts and survey geometry\n",
+    "apparent_resistivities = (\n",
+    "    simpeg.electromagnetics.static.utils.static_utils.apparent_resistivity_from_voltage(\n",
+    "        voltage_data.survey, voltage_data.dobs\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d3abdcae-d68d-4c56-8633-e25dc6804dd2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:41.481210Z",
+     "iopub.status.busy": "2026-04-13T23:24:41.480962Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.195237Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.194371Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:41.481185Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x275 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x275 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from simpeg.electromagnetics.static.utils.static_utils import (\n",
+    "    apparent_resistivity_from_voltage,\n",
+    "    plot_pseudosection,\n",
+    ")\n",
+    "\n",
+    "# Plot voltages pseudo-section\n",
+    "fig = plt.figure(figsize=(8, 2.75))\n",
+    "ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])\n",
+    "plot_pseudosection(\n",
+    "    voltage_data,\n",
+    "    plot_type=\"scatter\",\n",
+    "    ax=ax1,\n",
+    "    scale=\"log\",\n",
+    "    cbar_label=\"V/A\",\n",
+    "    scatter_opts={\"cmap\": \"viridis\"},\n",
+    ")\n",
+    "ax1.set_title(\"Normalized Voltages\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Get apparent conductivities from volts and survey geometry\n",
+    "apparent_resistivities = apparent_resistivity_from_voltage(\n",
+    "    voltage_data.survey, voltage_data.dobs\n",
+    ")\n",
+    "\n",
+    "# Plot apparent resistivity pseudo-section\n",
+    "fig = plt.figure(figsize=(8, 2.75))\n",
+    "ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])\n",
+    "plot_pseudosection(\n",
+    "    voltage_data.survey,\n",
+    "    apparent_resistivities,\n",
+    "    plot_type=\"contourf\",\n",
+    "    ax=ax1,\n",
+    "    scale=\"log\",\n",
+    "    cbar_label=r\"$\\Omega m$\",\n",
+    "    mask_topography=True,\n",
+    "    contourf_opts={\"levels\": 20, \"cmap\": \"RdYlBu\"},\n",
+    ")\n",
+    "ax1.set_title(\"Apparent Resistivity\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af6efa25-2e0c-4acb-8109-a4c241be128f",
+   "metadata": {},
+   "source": [
+    "## Create mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78fb6825-7a38-43c8-abf8-96177abe07d5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.195955Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.195732Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.238515Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.237688Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.195939Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dh = 4  # base cell width\n",
+    "dom_width_x = 3200.0  # domain width x\n",
+    "dom_width_z = 2400.0  # domain width z\n",
+    "nbcx = 2 ** int(np.round(np.log(dom_width_x / dh) / np.log(2.0)))  # num. base cells x\n",
+    "nbcz = 2 ** int(np.round(np.log(dom_width_z / dh) / np.log(2.0)))  # num. base cells z\n",
+    "\n",
+    "# Define the base mesh with top at z = 0 m\n",
+    "hx = [(dh, nbcx)]\n",
+    "hz = [(dh, nbcz)]\n",
+    "mesh = discretize.TreeMesh([hx, hz], x0=\"CN\", diagonal_balance=True)\n",
+    "\n",
+    "# Shift top to maximum topography\n",
+    "mesh.origin = mesh.origin + np.r_[0.0, topo_2d[:, -1].max()]\n",
+    "\n",
+    "# Mesh refinement based on topography\n",
+    "mesh.refine_surface(\n",
+    "    topo_2d,\n",
+    "    padding_cells_by_level=[0, 0, 4, 4],\n",
+    "    finalize=False,\n",
+    ")\n",
+    "\n",
+    "# Extract unique electrode locations.\n",
+    "unique_locations = voltage_data.survey.unique_electrode_locations\n",
+    "\n",
+    "# Mesh refinement near electrodes.\n",
+    "mesh.refine_points(\n",
+    "    unique_locations, padding_cells_by_level=[8, 12, 6, 6], finalize=False\n",
+    ")\n",
+    "\n",
+    "mesh.finalize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ad513789-95df-41c1-a1c0-9b686fab6341",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.239082Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.238925Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.244103Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.243202Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.239067Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Indices of the active mesh cells from topography (e.g. cells below surface)\n",
+    "active_cells = discretize.utils.active_from_xyz(mesh, topo_2d)\n",
+    "\n",
+    "# number of active cells\n",
+    "n_active = np.sum(active_cells)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f9bceaab-ece7-4d57-8b58-734586d56c25",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.244720Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.244521Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.253709Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.252719Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.244701Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_56550/2532513052.py:2: FutureWarning: Argument ``option`` is deprecated in favor of ``topo_cell_cutoff`` and will be removed in SimPEG v0.27.0.\n",
+      "  voltage_data.survey.drape_electrodes_on_topography(mesh, active_cells, option=\"top\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Project electrodes to topography\n",
+    "voltage_data.survey.drape_electrodes_on_topography(mesh, active_cells, option=\"top\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab4f0313-e7cb-4352-b122-3912c3fec89a",
+   "metadata": {},
+   "source": [
+    "## Define simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "36c320ef-116c-4794-ab10-a64d1f15ccf6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.254525Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.254270Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.265795Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.265090Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.254500Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Map model parameters to all cells.\n",
+    "# We'll use log conductivities as model, so we need to pass an ExpMap\n",
+    "# to the simulation in order to convert log conductivities to conductivities.\n",
+    "log_conductivity_map = simpeg.maps.InjectActiveCells(\n",
+    "    mesh, active_cells, 1e-8\n",
+    ") * simpeg.maps.ExpMap(nP=n_active)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b381c61e-b219-47a2-9a7f-3f9b9b487179",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.266363Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.266185Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.278050Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.277400Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.266347Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Get median apparent resistivity\n",
+    "median_resistivity = np.median(apparent_resistivities)\n",
+    "median_conductivity = 1 / median_resistivity\n",
+    "\n",
+    "# TODO: this is not the median conduct, right?\n",
+    "# Shall we do median_conductivity = np.median(1 / apparent_resistivities)?\n",
+    "\n",
+    "# Create initial model (log conductivity)\n",
+    "initial_model = np.log(1 / median_resistivity) * np.ones(n_active)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "777cf480-633c-4a1f-88c3-ae027697aba7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.278568Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.278412Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.664104Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.663320Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.278553Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "solver = simpeg.utils.solver_utils.get_default_solver()\n",
+    "simulation_simpeg = dc.simulation_2d.Simulation2DNodal(\n",
+    "    mesh,\n",
+    "    survey=voltage_data.survey,\n",
+    "    sigmaMap=log_conductivity_map,\n",
+    "    storeJ=True,\n",
+    "    solver=solver,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "91453297-bb70-4207-9e2a-eb15f8589b01",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.664789Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.664616Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.668198Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.667144Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.664773Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Assign a model to the simulation, just to set the correct size.\n",
+    "# We should improve this, allowing simulations to inform about the size of the expected model.\n",
+    "simulation_simpeg.model = initial_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dba8f16-e412-43a1-8607-0160c0bf54af",
+   "metadata": {},
+   "source": [
+    "## Create inversion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebdad7ac-7685-4224-9453-747bdc906e72",
+   "metadata": {},
+   "source": [
+    "Wrap the simulation into an object that can be used in the new framework."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "445f4b23-8877-49e6-8b4a-33049e731855",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.668840Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.668652Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.680417Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.679674Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.668824Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<inversion_ideas.simulations.WrappedSimulation at 0x7f9204affe00>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: we should be able to set store_jacobian to True,\n",
+    "# but sensitivity weights don't currently support jacobians as LinearOperators.\n",
+    "simulation = ii.wrap_simulation(simulation_simpeg, store_jacobian=True)\n",
+    "simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "765c906a-a191-40be-951b-c5231f2f792b",
+   "metadata": {},
+   "source": [
+    "Extract data and uncertainties from data object and build the data misfit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9320114b-435f-48f8-8ec1-d830678e2a3c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.681653Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.681331Z",
+     "iopub.status.idle": "2026-04-13T23:24:42.689167Z",
+     "shell.execute_reply": "2026-04-13T23:24:42.688356Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.681625Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data = voltage_data.dobs\n",
+    "uncertainties = voltage_data.standard_deviation\n",
+    "\n",
+    "data_misfit = ii.DataMisfit(data, uncertainties, simulation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672621bd-a7db-413d-8a71-b5dd034630c5",
+   "metadata": {},
+   "source": [
+    "Define regularization (smallness) with sensitivity weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9042f903-4d02-4abe-9de1-2dfacfb3b3a0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:42.690009Z",
+     "iopub.status.busy": "2026-04-13T23:24:42.689792Z",
+     "iopub.status.idle": "2026-04-13T23:24:47.768623Z",
+     "shell.execute_reply": "2026-04-13T23:24:47.767804Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:42.689985Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.06 \\, \\phi_{s} (m) + \\phi_{x} (m) + \\phi_{y} (m)$"
+      ],
+      "text/plain": [
+       "0.06 φs(m) + φx(m) + φy(m)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get sensitivity weights\n",
+    "volumes = mesh.cell_volumes[active_cells]\n",
+    "sensitivity_weights = ii.utils.get_sensitivity_weights(\n",
+    "    simulation.jacobian(initial_model),\n",
+    "    data_weights=data_misfit.weights_matrix,\n",
+    "    volumes=volumes,\n",
+    "    vmin=0.01,\n",
+    ")\n",
+    "weights_dict = {\n",
+    "    \"sensitivity\": sensitivity_weights,\n",
+    "}\n",
+    "\n",
+    "# Use the initial model as our reference model\n",
+    "reference_model = initial_model.copy()\n",
+    "\n",
+    "alpha_s = 0.06\n",
+    "smallness = ii.Smallness(\n",
+    "    mesh=mesh,\n",
+    "    active_cells=active_cells,\n",
+    "    cell_weights=weights_dict,\n",
+    "    reference_model=initial_model,\n",
+    ")\n",
+    "smoothness_x, smoothness_y = tuple(\n",
+    "    ii.Flatness(\n",
+    "        mesh=mesh,\n",
+    "        active_cells=active_cells,\n",
+    "        direction=direction,\n",
+    "        cell_weights=weights_dict,\n",
+    "    )\n",
+    "    for direction in (\"x\", \"y\")\n",
+    ")\n",
+    "\n",
+    "model_norm = (alpha_s * smallness + smoothness_x + smoothness_y).flatten()\n",
+    "model_norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "660fd862-517a-4b2d-8589-6e234c239c76",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:47.769594Z",
+     "iopub.status.busy": "2026-04-13T23:24:47.769355Z",
+     "iopub.status.idle": "2026-04-13T23:24:47.774703Z",
+     "shell.execute_reply": "2026-04-13T23:24:47.773819Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:47.769571Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.01), np.float64(1.0))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sensitivity_weights.min(), sensitivity_weights.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a7034b-3b5a-4170-a65f-911281fba9bd",
+   "metadata": {},
+   "source": [
+    "Build objective function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e81a3051-9874-40f6-95ca-d1f8559ec1bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:47.775538Z",
+     "iopub.status.busy": "2026-04-13T23:24:47.775306Z",
+     "iopub.status.idle": "2026-04-13T23:24:47.789402Z",
+     "shell.execute_reply": "2026-04-13T23:24:47.788547Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:47.775517Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\phi_{d} (m) + 5. \\, [0.06 \\, \\phi_{s} (m) + \\phi_{x} (m) + \\phi_{y} (m)]$"
+      ],
+      "text/plain": [
+       "φd(m) + 5. [0.06 φs(m) + φx(m) + φy(m)]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "beta_0 = 5\n",
+    "regularization = beta_0 * model_norm\n",
+    "phi = data_misfit + regularization\n",
+    "phi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15e61bb9-3bf8-4d0c-b99f-4b9b1f2c674e",
+   "metadata": {},
+   "source": [
+    "## Gauss-Newton"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790a1a7c-00b3-46e1-b1ef-11ffc43719da",
+   "metadata": {},
+   "source": [
+    "Minimize the objective function with a fixed beta through Gauss Newton. The `ii.GaussNewtonConjugateGradient` minimizer builds a generator when called. We can iterate over it and get the model after each Gauss-Newton iteration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc7a24ef-8c62-4293-a1b4-364f1172c86c",
+   "metadata": {},
+   "source": [
+    "Define a BFGS preconditioner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bfed4765-23e1-4863-a804-9dbaf9ea9849",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:47.790315Z",
+     "iopub.status.busy": "2026-04-13T23:24:47.790070Z",
+     "iopub.status.idle": "2026-04-13T23:24:47.829691Z",
+     "shell.execute_reply": "2026-04-13T23:24:47.828652Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:47.790291Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Estimate the initial estimate for the BFGS preconditioner as the Jacobi\n",
+    "# preconditioner of the regularization.\n",
+    "# This is useful when we don't want to (or can't) compute the jacobi of the data misfit.\n",
+    "initial_matrix = ii.get_jacobi_preconditioner(regularization, initial_model)\n",
+    "preconditioner = ii.BFGSPreconditioner(phi, initial_matrix=initial_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0fef5bbc-c667-40d6-ab19-3f316a4c533a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:24:47.830637Z",
+     "iopub.status.busy": "2026-04-13T23:24:47.830381Z",
+     "iopub.status.idle": "2026-04-13T23:25:40.400653Z",
+     "shell.execute_reply": "2026-04-13T23:25:40.399682Z",
+     "shell.execute_reply.started": "2026-04-13T23:24:47.830613Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished iteration  0\n",
+      "  phi(model) = 4.42e+04\n",
+      "Finished iteration  1\n",
+      "  phi(model) = 1.26e+04\n",
+      "Finished iteration  2\n",
+      "  phi(model) = 3.90e+03\n",
+      "Finished iteration  3\n",
+      "  phi(model) = 1.44e+03\n",
+      "Finished iteration  4\n",
+      "  phi(model) = 7.67e+02\n",
+      "Finished iteration  5\n",
+      "  phi(model) = 5.92e+02\n",
+      "Finished iteration  6\n",
+      "  phi(model) = 5.48e+02\n",
+      "Finished iteration  7\n",
+      "  phi(model) = 5.33e+02\n"
+     ]
+    }
+   ],
+   "source": [
+    "gauss_newton_minimizer = ii.GaussNewtonConjugateGradient(rtol=0.05)\n",
+    "gauss_newton_generator = gauss_newton_minimizer(\n",
+    "    phi, initial_model, preconditioner=preconditioner\n",
+    ")\n",
+    "\n",
+    "for i, model in enumerate(gauss_newton_generator):\n",
+    "    print(f\"Finished iteration {i:2d}\")\n",
+    "    print(f\"  phi(model) = {phi(model):.2e}\")\n",
+    "\n",
+    "inverted_model = model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "12b50f2b-eba2-456a-8974-ed1297644970",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:25:40.401868Z",
+     "iopub.status.busy": "2026-04-13T23:25:40.401547Z",
+     "iopub.status.idle": "2026-04-13T23:25:40.408195Z",
+     "shell.execute_reply": "2026-04-13T23:25:40.407429Z",
+     "shell.execute_reply.started": "2026-04-13T23:25:40.401820Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.8321826927140525)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_misfit.chi_factor(inverted_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6af9c936-5cf8-43bd-bfcc-e857715de2bd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:25:40.409396Z",
+     "iopub.status.busy": "2026-04-13T23:25:40.409126Z",
+     "iopub.status.idle": "2026-04-13T23:25:40.422120Z",
+     "shell.execute_reply": "2026-04-13T23:25:40.420991Z",
+     "shell.execute_reply.started": "2026-04-13T23:25:40.409369Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "inverted_conductivity = np.exp(inverted_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fa229006-959d-47a4-bffd-c91bc57a04d4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:25:40.423283Z",
+     "iopub.status.busy": "2026-04-13T23:25:40.422978Z",
+     "iopub.status.idle": "2026-04-13T23:25:40.773341Z",
+     "shell.execute_reply": "2026-04-13T23:25:40.772561Z",
+     "shell.execute_reply.started": "2026-04-13T23:25:40.423252Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "norm = LogNorm()\n",
+    "plotting_map = simpeg.maps.InjectActiveCells(mesh, active_cells, np.nan)\n",
+    "\n",
+    "fig = plt.figure(figsize=(9, 4))\n",
+    "\n",
+    "ax1 = fig.add_axes([0.14, 0.17, 0.68, 0.7])\n",
+    "(tmp,) = mesh.plot_image(\n",
+    "    plotting_map * inverted_conductivity,\n",
+    "    normal=\"Y\",\n",
+    "    ax=ax1,\n",
+    "    grid=False,\n",
+    "    pcolor_opts={\"norm\": norm, \"cmap\": \"RdYlBu_r\"},\n",
+    ")\n",
+    "ax1.set_xlim(-500, 500)\n",
+    "ax1.set_ylim(-300, 200)\n",
+    "ax1.set_title(\"Recovered Conductivity Model (L2)\")\n",
+    "ax1.set_xlabel(\"x (m)\")\n",
+    "ax1.set_ylabel(\"z (m)\")\n",
+    "\n",
+    "# ax2 = fig.add_axes([0.84, 0.17, 0.03, 0.7])\n",
+    "# cbar = mpl.colorbar.ColorbarBase(\n",
+    "#     ax2, norm=norm, orientation=\"vertical\", cmap=\"RdYlBu_r\"\n",
+    "# )\n",
+    "plt.colorbar(tmp, ax=ax1, norm=norm, orientation=\"vertical\")\n",
+    "# cbar.set_label(r\"$\\sigma$ (S/m)\", rotation=270, labelpad=15, size=12)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f71e64de-b9dd-45c7-a52d-e6cdc69040d2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-13T23:25:40.774348Z",
+     "iopub.status.busy": "2026-04-13T23:25:40.774093Z",
+     "iopub.status.idle": "2026-04-13T23:25:41.061430Z",
+     "shell.execute_reply": "2026-04-13T23:25:41.060408Z",
+     "shell.execute_reply.started": "2026-04-13T23:25:40.774326Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x275 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x275 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dpred = simulation(inverted_model)\n",
+    "\n",
+    "\n",
+    "prediction = simpeg.Data(voltage_data.survey, dobs=dpred)\n",
+    "\n",
+    "# Plot voltages pseudo-section\n",
+    "fig = plt.figure(figsize=(8, 2.75))\n",
+    "ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])\n",
+    "plot_pseudosection(\n",
+    "    prediction,\n",
+    "    plot_type=\"scatter\",\n",
+    "    ax=ax1,\n",
+    "    scale=\"log\",\n",
+    "    cbar_label=\"V/A\",\n",
+    "    scatter_opts={\"cmap\": \"viridis\"},\n",
+    ")\n",
+    "ax1.set_title(\"Normalized Voltages\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Get apparent conductivities from volts and survey geometry\n",
+    "apparent_resistivities = apparent_resistivity_from_voltage(voltage_data.survey, dpred)\n",
+    "\n",
+    "# Plot apparent resistivity pseudo-section\n",
+    "fig = plt.figure(figsize=(8, 2.75))\n",
+    "ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])\n",
+    "plot_pseudosection(\n",
+    "    voltage_data.survey,\n",
+    "    apparent_resistivities,\n",
+    "    plot_type=\"contourf\",\n",
+    "    ax=ax1,\n",
+    "    scale=\"log\",\n",
+    "    cbar_label=r\"$\\Omega m$\",\n",
+    "    mask_topography=True,\n",
+    "    contourf_opts={\"levels\": 20, \"cmap\": \"RdYlBu\"},\n",
+    ")\n",
+    "ax1.set_title(\"Apparent Resistivity\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:inversion_ideas]",
+   "language": "python",
+   "name": "conda-env-inversion_ideas-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/inversion_ideas/__init__.py b/src/inversion_ideas/__init__.py
index fb407d1..ee35f40 100644
--- a/src/inversion_ideas/__init__.py
+++ b/src/inversion_ideas/__init__.py
@@ -15,7 +15,11 @@
 from .inversion import Inversion
 from .inversion_log import InversionLog, InversionLogRich
 from .minimize import GaussNewtonConjugateGradient, conjugate_gradient
-from .preconditioners import JacobiPreconditioner, get_jacobi_preconditioner
+from .preconditioners import (
+    BFGSPreconditioner,
+    JacobiPreconditioner,
+    get_jacobi_preconditioner,
+)
 from .recipes import (
     create_l2_inversion,
     create_sparse_inversion,
@@ -25,6 +29,7 @@
 from .simulations import wrap_simulation
 
 __all__ = [
+    "BFGSPreconditioner",
     "ChiTarget",
     "ConvergenceWarning",
     "CustomCondition",
diff --git a/src/inversion_ideas/minimize/_functions.py b/src/inversion_ideas/minimize/_functions.py
index 95b95aa..67a0f24 100644
--- a/src/inversion_ideas/minimize/_functions.py
+++ b/src/inversion_ideas/minimize/_functions.py
@@ -5,19 +5,18 @@
 """
 
 import warnings
-from collections.abc import Callable
 
 from scipy.sparse.linalg import cg
 
 from ..base import Objective
 from ..errors import ConvergenceWarning
-from ..typing import Model, Preconditioner
+from ..typing import CanBeUpdated, Model, Preconditioner
 
 
 def conjugate_gradient(
     objective: Objective,
     initial_model: Model,
-    preconditioner: Preconditioner | Callable[[Model], Preconditioner] | None = None,
+    preconditioner: Preconditioner | None = None,
     **kwargs,
 ) -> Model:
     r"""
@@ -33,12 +32,11 @@ def conjugate_gradient(
         Objective function to be minimized.
     initial_model : (n_params) array
         Initial model used to start the minimization.
-    preconditioner : (n_params, n_params) array, sparse array or LinearOperator or Callable, optional
+    preconditioner : (n_params, n_params) array, sparse array or LinearOperator, optional
         Matrix used as preconditioner in the conjugant gradient algorithm.
         If None, no preconditioner will be used.
-        A callable can be passed to build the preconditioner dynamically: such
-        callable should take a single ``initial_model`` argument and return an
-        array, sparse array or a `LinearOperator`.
+        If the preconditioner implements an ``initialize`` method, the preconditioner
+        will be initialized before being used in the conjugate gradient algorithm.
     kwargs : dict
         Extra arguments that will be passed to the :func:`scipy.sparse.linalg.cg`
         function.
@@ -65,8 +63,8 @@ def conjugate_gradient(
         raise ValueError(msg)
 
     if preconditioner is not None:
-        if callable(preconditioner):
-            preconditioner = preconditioner(initial_model)
+        if isinstance(preconditioner, CanBeUpdated):
+            preconditioner.update(initial_model)
         kwargs["M"] = preconditioner
 
     # TODO: maybe it would be nice to add a `is_linear` attribute to the objective
diff --git a/src/inversion_ideas/minimize/_minimizers.py b/src/inversion_ideas/minimize/_minimizers.py
index 0005be8..e32d930 100644
--- a/src/inversion_ideas/minimize/_minimizers.py
+++ b/src/inversion_ideas/minimize/_minimizers.py
@@ -11,7 +11,7 @@
 
 from ..base import Condition, Minimizer, MinimizerResult, Objective
 from ..errors import ConvergenceWarning
-from ..typing import Model, Preconditioner
+from ..typing import CanBeUpdated, Model, Preconditioner
 from ..utils import CountCalls, Counter, get_logger
 from ._utils import backtracking_line_search
 
@@ -65,9 +65,7 @@ def __call__(
         objective: Objective,
         initial_model: Model,
         *,
-        preconditioner: (
-            Preconditioner | Callable[[Model], Preconditioner] | None
-        ) = None,
+        preconditioner: Preconditioner | None = None,
         callback: Callable[[MinimizerResult], None] | None = None,
     ) -> Generator[Model]:
         """
@@ -76,30 +74,23 @@ def __call__(
         Parameters
         ----------
         objective : Objective
-            Objective function that will get minimized.
+            Objective function to be minimized.
         initial_model : (n_params) array
-            Initial model to start the minimization.
-        preconditioner : (n_params, n_params) array, sparray or LinearOperator or Callable, optional
+            Initial model used to start the minimization.
+        preconditioner : (n_params, n_params) array, sparse array or LinearOperator, optional
             Matrix used as preconditioner in the conjugant gradient algorithm.
             If None, no preconditioner will be used.
-            A callable can be passed to build the preconditioner dynamically: such
-            callable should take a single ``initial_model`` argument and return an
-            array, `sparray` or a `LinearOperator`.
+            If the preconditioner implements an ``update`` method, the preconditioner
+            will be updated **before** every conjugate gradient minimization.
         callback : callable, optional
             Callable that gets called after each iteration.
         """
+        # Add the preconditioner to kwargs that will be passed to the cg function
         cg_kwargs = self.cg_kwargs.copy()
-
-        # Define a static preconditioner for all Gauss-Newton iterations
         if preconditioner is not None:
             if "M" in self.cg_kwargs:
                 msg = "Cannot simultanously pass `preconditioner` and `M`."
                 raise ValueError(msg)
-            preconditioner = (
-                preconditioner
-                if not callable(preconditioner)
-                else preconditioner(initial_model)
-            )
             cg_kwargs["M"] = preconditioner
 
         # Perform Gauss-Newton iterations
@@ -146,6 +137,13 @@ def __call__(
             if self.stopping_criterion is not None and self.stopping_criterion(model):
                 break
 
+            # Compute gradient and hessian
+            gradient, hessian = objective.gradient(model), objective.hessian(model)
+
+            # Update preconditioner before running cg
+            if isinstance(preconditioner, CanBeUpdated):
+                preconditioner.update(model)
+
             # Add a counter for conjugate-gradient iterations
             if (key := "callback") not in cg_kwargs:
                 counter = Counter()
@@ -160,6 +158,7 @@ def __call__(
 
             # Get number of cg iterations from callback
             cg_iters = cg_kwargs["callback"].counts
+
             # Raise warning if cg didn't converge
             cg_converged = cg_code == 0
             if not cg_converged:
diff --git a/src/inversion_ideas/operators.py b/src/inversion_ideas/operators.py
index 04cae29..27267a7 100644
--- a/src/inversion_ideas/operators.py
+++ b/src/inversion_ideas/operators.py
@@ -1,5 +1,5 @@
 """
-Custom LinearOperator classes and utilities.
+Custom :class:`scipy.sparse.linalg.LinearOperator` classes.
 """
 
 import numpy as np
@@ -9,6 +9,46 @@
 from inversion_ideas.typing import HasDiagonal, SparseArray
 
 
+class Identity(LinearOperator):
+    """
+    Identity matrix.
+
+    Represent the identity matrix of size ``n`` through
+    a :class:`scipy.sparse.linalg.LinearOperator`, to avoid storing a large diagonal
+    array full of ones.
+
+    Parameters
+    ----------
+    n : int
+        Size of the square matrix.
+    dtype : dtype, optional
+        Data type used for the linear operator.
+    """
+
+    def __init__(self, n: int, dtype=np.float64):
+        self._n = n
+        super().__init__(shape=(n, n), dtype=dtype)
+
+    @property
+    def n(self):
+        """
+        Size of the identity matrix.
+        """
+        return self._n
+
+    def _matvec(self, x):
+        return x.copy()
+
+    def _adjoint(self):
+        return self
+
+    def diagonal(self):
+        """
+        Diagonal of the matrix.
+        """
+        return np.ones(self.n, dtype=self.dtype)
+
+
 def get_diagonal(operator: npt.NDArray | SparseArray | LinearOperator):
     r"""
     Extract diagonal of a linear operator.
diff --git a/src/inversion_ideas/preconditioners.py b/src/inversion_ideas/preconditioners.py
index 49e5dd7..b427956 100644
--- a/src/inversion_ideas/preconditioners.py
+++ b/src/inversion_ideas/preconditioners.py
@@ -2,52 +2,67 @@
 Classes and functions to build preconditioners.
 """
 
-from scipy.sparse import diags_array
+import warnings
+
+import numpy as np
+import numpy.typing as npt
+from scipy.sparse import dia_array, diags_array
+from scipy.sparse.linalg import LinearOperator, aslinearoperator
 
 from .base import Objective
+from .operators import Identity
 from .typing import Model, SparseArray
 
 
-class JacobiPreconditioner:
+class JacobiPreconditioner(LinearOperator):
     """
     Jacobi preconditioner for a given objective function.
 
     Use this class to define a dynamic Jacobi preconditioner from an objective function.
-    This class is a callable that will update the preconditioner for the given model
-    each time it gets called. Use this class if you want to update the preconditioner
-    on every iteration of the `Inversion`.
+    This class implements the ``update`` method that can be used by minimizers to
+    dynamically update the preconditioner.
 
     Parameters
     ----------
     objective_function : Objective
         Objective function for which the Jacobi preconditioner will be built.
+    dtype : dtype, optional
+        Data type of the matrix.
 
     See Also
     --------
     get_jacobi_preconditioner
     """
 
-    def __init__(self, objective_function: Objective):
+    def __init__(self, objective_function: Objective, dtype=np.float64):
         self.objective_function = objective_function
+        n = self.objective_function.n_params
+        super().__init__(shape=(n, n), dtype=dtype)
+
+    @property
+    def A(self) -> dia_array:
+        if not hasattr(self, "_preconditioner"):
+            msg = (
+                f"Preconditioner {self} doesn't have a `A` attribute "
+                "since it hasn't been initialized yet."
+            )
+            raise AttributeError(msg)
+        return self._preconditioner
 
-    def __call__(self, model: Model) -> SparseArray:
+    def update(self, model: Model):
         """
-        Generate a Jacobi preconditioner as a sparse diagonal array for a given model.
+        Update the preconditioner.
+        """
+        self._preconditioner = get_jacobi_preconditioner(self.objective_function, model)
 
-        Parameters
-        ----------
-        model : (n_params) array
-            Model that will be used to build the Jacobi preconditioner from the
-            ``objective_function``.
+    def _matvec(self, x):
+        return self.A @ x
 
-        Returns
-        -------
-        dia_array
-        """
-        return get_jacobi_preconditioner(self.objective_function, model)
+    def _rmatvec(self, x):
+        return self.A.T @ x
 
 
-def get_jacobi_preconditioner(objective_function: Objective, model: Model):
+def get_jacobi_preconditioner(objective_function: Objective, model: Model) -> dia_array:
     r"""
     Obtain a Jacobi preconditioner from an objective function.
 
@@ -84,3 +99,160 @@ def get_jacobi_preconditioner(objective_function: Objective, model: Model):
     preconditioner[~zeros] **= -1
 
     return diags_array(preconditioner)
+
+
+class BFGSPreconditioner(LinearOperator):
+    r"""
+    BFGS Preconditioner.
+
+    Use this class to define a dynamic BFGS preconditioner from an objective function.
+    This class implements the ``update`` method that can be used by minimizers to
+    dynamically update the preconditioner.
+
+    Parameters
+    ----------
+    objective_function : Objective
+        Objective function for which the Jacobi preconditioner will be built.
+    initial_matrix : array or sparray or LinearOperator or None, optional
+        Square matrix that will be used as the initial estimate of the inverse of the
+        Hessian of the ``objective_function``.
+        If None, the identity matrix will be used.
+    dtype : dtype, optional
+        Data type of the matrix.
+
+    Notes
+    -----
+    The preconditioner is built using the BFGS update equation (Nocedal & Wright, 1999,
+    eq. 8.16):
+
+    .. math::
+
+        H_{k+1} = (I - \rho_k s_k y_k^T) H_k (I - \rho_k y_k s_k^T) + \rho s_k s_k^T,
+
+    where
+
+    .. math::
+
+        \rho_k = \frac{1}{y_k^T s_k},
+
+    .. math::
+
+        \y_k = \nabla \phi(\mathbf{m}_{k+1}) - \nabla \phi(\mathbf{m}_k),
+
+    .. math::
+
+        \s_k = m_{k+1} - m_k,
+
+    and :math:`\phi(\cdot)` is the objective function that will be inverted.
+
+    By default, the :math:`H_0` is set as the identity matrix. Alternatively, the user
+    can set an initial value for it through the ``initial_preconditioner`` argument.
+
+    References
+    ----------
+    Nocedal, J., & Wright, S. J. (1999). Numerical optimization. Springer.
+    """
+
+    def __init__(
+        self,
+        objective_function: Objective,
+        initial_matrix: (
+            npt.NDArray[np.float64] | SparseArray | LinearOperator | None
+        ) = None,
+        dtype=np.float64,
+    ):
+        n = objective_function.n_params
+        self.objective_function = objective_function
+        super().__init__(shape=(n, n), dtype=dtype)
+        self.initial_matrix = (
+            initial_matrix if initial_matrix is not None else Identity(n)
+        )
+        self._index = None
+
+    @property
+    def index(self) -> int | None:
+        """
+        Current index of the BFGS update algorithm.
+        """
+        return self._index
+
+    @property
+    def matrix(self) -> LinearOperator:
+        """
+        Current preconditioner matrix :math:`H_k`.
+        """
+        if not hasattr(self, "_matrix"):
+            return aslinearoperator(self.initial_matrix)
+        return self._matrix
+
+    def _matvec(self, x):
+        return self.matrix @ x
+
+    def _rmatvec(self, x):
+        return self.matrix.T @ x
+
+    def reset(self):
+        """
+        Reset the BFGS preconditioner.
+
+        Ditch the current preconditioner matrix estimation and start over with the
+        initial matrix. Resets the ``index`` to None.
+        """
+        self._index = None
+        del self._matrix
+        del self._model_k
+        del self._gradient_k
+
+    def update(self, model: Model):
+        """
+        Update the BFGS preconditioner.
+
+        Parameters
+        ----------
+        model : (n_params,) array
+            Model array.
+        """
+        # Get new model and gradient (cast model and gradient as vertical vectors).
+        new_model = model[:, None].copy()
+        new_gradient = self.objective_function.gradient(model)[:, None]
+
+        if self.index is None:
+            # Define the first matrix as a LinearOperator containing the initial matrix
+            self._matrix = aslinearoperator(self.initial_matrix)
+            self._index = 0
+        else:
+            # Compute variables to update the preconditioner
+            s_k = new_model - self._model_k
+            y_k = new_gradient - self._gradient_k
+            (y_dot_s,) = (
+                y_k.T @ s_k
+            ).ravel()  # extract the single element from the 2d array
+            rho_k = 1 / y_dot_s
+            s_k, y_k = aslinearoperator(y_k), aslinearoperator(s_k)
+            eye = Identity(self.objective_function.n_params)
+
+            if y_dot_s <= 0:
+                msg = (
+                    "Found `y.T @ s <= 0` while updating BFGS preconditioner. "
+                    "Skipping updating step."
+                    "The skipping process is still in experimental phase, "
+                    "please open an issue if you received this warning!"
+                )
+                warnings.warn(msg, stacklevel=2)
+
+                # Cache model and gradient
+                self._model_k = new_model
+                self._gradient_k = new_gradient
+                self._index += 1
+                return
+
+            # Update the preconditioner
+            left = eye - rho_k * (s_k @ y_k.T)
+            right = eye - rho_k * (y_k @ s_k.T)
+            self._matrix = left @ self._matrix @ right + rho_k * (s_k @ s_k.T)
+
+            self._index += 1
+
+        # Cache model and gradient
+        self._model_k = new_model
+        self._gradient_k = new_gradient
diff --git a/src/inversion_ideas/recipes.py b/src/inversion_ideas/recipes.py
index 6b7bf5c..460b2d3 100644
--- a/src/inversion_ideas/recipes.py
+++ b/src/inversion_ideas/recipes.py
@@ -3,6 +3,7 @@
 """
 
 from collections.abc import Callable
+from typing import Literal
 
 import numpy as np
 import numpy.typing as npt
@@ -13,7 +14,11 @@
 from .directives import Irls, MultiplierCooler
 from .inversion import Inversion
 from .inversion_log import Column
-from .preconditioners import JacobiPreconditioner
+from .preconditioners import (
+    BFGSPreconditioner,
+    JacobiPreconditioner,
+    get_jacobi_preconditioner,
+)
 from .regularization import Flatness, Smallness
 from .typing import Model, Preconditioner
 
@@ -30,7 +35,7 @@ def create_l2_inversion(
     chi_target: float = 1.0,
     max_iterations: int | None = None,
     cache_models: bool = True,
-    preconditioner: Preconditioner | Callable[[Model], Preconditioner] | None = None,
+    preconditioner: Literal["bfgs", "jacobi"] | Preconditioner | None = None,
 ) -> Inversion:
     r"""
     Create inversion of the form :math:`\phi_d + \beta \phi_m`.
@@ -66,14 +71,15 @@ def create_l2_inversion(
         no limit on the total amount of iterations.
     cache_models : bool, optional
         Whether to cache models after each iteration in the inversion.
-    preconditioner : {"jacobi"} or 2d array or sparse array or LinearOperator or callable or None, optional
+    preconditioner : {"jacobi"} or 2d array or sparse array or LinearOperator or None, optional
         Preconditioner that will be passed to the ``minimizer`` on every call during the
-        inversion. The preconditioner can be a predefined 2d array, a sparse array or
-        a LinearOperator. Alternatively, it can be a callable that takes the ``model``
-        as argument and returns a preconditioner matrix (same types listed before). If
-        ``"jacobi"``, a default Jacobi preconditioner that will get updated on every
-        iteration will be defined for the inversion. If None, no preconditioner will be
-        passed.
+        inversion.
+        If ``"bfgs"``, a default BFGS preconditioner will be used, where the initial
+        estimate for it will be set as the Jacobi preconditioner of the ``model_norm``
+        times the ``starting_beta``.
+        If ``"jacobi"``, a default Jacobi preconditioner that will get updated on every
+        iteration will be defined for the inversion.
+        If None, no preconditioner will be passed.
 
     Returns
     -------
@@ -99,8 +105,14 @@ def create_l2_inversion(
     minimizer_kwargs = {}
     if preconditioner is not None:
         if isinstance(preconditioner, str):
+            preconditioner = preconditioner.lower()
             if preconditioner == "jacobi":
                 preconditioner = JacobiPreconditioner(objective_function)
+            elif preconditioner == "bfgs":
+                initial_matrix = get_jacobi_preconditioner(
+                    starting_beta * model_norm, initial_model
+                )
+                preconditioner = BFGSPreconditioner(objective_function, initial_matrix)
             else:
                 msg = f"Invalid preconditioner '{preconditioner}'."
                 raise ValueError(msg)
@@ -134,7 +146,7 @@ def create_sparse_inversion(
     model_norm_rtol: float = 1e-3,
     max_iterations: int | None = None,
     cache_models: bool = True,
-    preconditioner: Preconditioner | Callable[[Model], Preconditioner] | None = None,
+    preconditioner: Literal["bfgs", "jacobi"] | Preconditioner | None = None,
 ) -> Inversion:
     r"""
     Create sparse norm inversion of the form: :math:`\phi_d + \beta \phi_m`.
@@ -182,14 +194,15 @@ def create_sparse_inversion(
         no limit on the total amount of iterations.
     cache_models : bool, optional
         Whether to cache models after each iteration in the inversion.
-    preconditioner : {"jacobi"} or 2d array or sparse array or LinearOperator or callable or None, optional
+    preconditioner : {"jacobi"} or 2d array or sparse array or LinearOperator or None, optional
         Preconditioner that will be passed to the ``minimizer`` on every call during the
-        inversion. The preconditioner can be a predefined 2d array, a sparse array or
-        a LinearOperator. Alternatively, it can be a callable that takes the ``model``
-        as argument and returns a preconditioner matrix (same types listed before). If
-        ``"jacobi"``, a default Jacobi preconditioner that will get updated on every
-        iteration will be defined for the inversion. If None, no preconditioner will be
-        passed.
+        inversion.
+        If ``"bfgs"``, a default BFGS preconditioner will be used, where the initial
+        estimate for it will be set as the Jacobi preconditioner of the ``model_norm``
+        times the ``starting_beta``.
+        If ``"jacobi"``, a default Jacobi preconditioner that will get updated on every
+        iteration will be defined for the inversion.
+        If None, no preconditioner will be passed.
 
     Returns
     -------
@@ -217,8 +230,14 @@ def create_sparse_inversion(
     minimizer_kwargs = {}
     if preconditioner is not None:
         if isinstance(preconditioner, str):
+            preconditioner = preconditioner.lower()
             if preconditioner == "jacobi":
                 preconditioner = JacobiPreconditioner(objective_function)
+            elif preconditioner == "bfgs":
+                initial_matrix = get_jacobi_preconditioner(
+                    starting_beta * model_norm, initial_model
+                )
+                preconditioner = BFGSPreconditioner(objective_function, initial_matrix)
             else:
                 msg = f"Invalid preconditioner '{preconditioner}'."
                 raise ValueError(msg)
diff --git a/src/inversion_ideas/typing.py b/src/inversion_ideas/typing.py
index 726461d..bbf4dcb 100644
--- a/src/inversion_ideas/typing.py
+++ b/src/inversion_ideas/typing.py
@@ -26,13 +26,22 @@
 
 Preconditioner: TypeAlias = npt.NDArray[np.float64] | SparseArray | LinearOperator
 """
-Type for static preconditioners.
+Type for preconditioners.
 
-Static preconditioners can either be a dense matrix, a sparse matrix or
-a ``LinearOperator``.
+Preconditioners can either be a dense matrix, a sparse matrix or a ``LinearOperator``.
 """
 
 
+@runtime_checkable
+class CanBeUpdated(Protocol):
+    """
+    Protocol for objects that can be updated.
+    """
+
+    def update(self, model: Model) -> None:
+        raise NotImplementedError
+
+
 class SparseRegularization(Protocol):
     """
     Protocol to define sparse regularizations that can be used with a IRLS algorithm.