{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a3c80085", "metadata": { "execution": { "iopub.execute_input": "2025-03-15T11:24:20.846364Z", "iopub.status.busy": "2025-03-15T11:24:20.846037Z", "iopub.status.idle": "2025-03-15T11:24:22.404153Z", "shell.execute_reply": "2025-03-15T11:24:22.403772Z" } }, "outputs": [], "source": [ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import preliz as pz\n", "import pymc as pm\n", "\n", "import gEconpy as ge\n", "import gEconpy.plotting as gp\n", "\n", "seed = sum(map(ord, \"Two Households Example\"))\n", "rng = np.random.default_rng(seed)\n", "\n", "gp.set_matplotlib_style()" ] }, { "cell_type": "markdown", "id": "c1b90770", "metadata": {}, "source": [ "# A Simple Two-Household Model\n", "\n", "In this model, we take a look at a simple model with two households. These are sometimes called TANK models, for \"Two Agent New Keynesian\". I'm not including nominal frictions though, so it's not really NK and I'll refrain from calling it that.\n", "\n", "This model is interesting because it's a case where we can't fully describe the steady state. We will see that in these cases, gEconpy is able to automatically work out how to deploy an optimizer. The user is able to partially describe the steady state, and this information will also be incorporated.\n", "\n", "The down side of this is that Bayesian estimation of the model is not available. Currently, this requires that the model steady state be analytically described. I hope to change this in the near future, though! To circumvent this, we also study a strategically simplified two-household model that has an analytic steady state. We examine what is lost in terms of expressive power, and a parameter recovery exercise is peformed. " ] }, { "cell_type": "markdown", "id": "983db2cf", "metadata": {}, "source": [ "## A bunch of algebra\n", "\n", "In the model, we will have a fraction $\\omega \\in (0, 1)$ of individuals coming from the \"Ricardian\" household, and $1 - \\omega$ coming from the \"non-Ricardian\" household. A household is described as \"Ricardian\" if it has access to financial markets. Ricardian households save by investing in installed capital, and recieve rents on that capital from firms. Non-Ricardian households, on the other hand, cannot invest. They live \"hand to mouth\", spending all of their income on consumption.\n", "\n", "Total consumption and labor across the economy is additive, so $C_t = \\omega C_{t,R} + (1 - \\omega) C_{t, NR}$ and $L_t = \\omega L_{t,R} + (1 - \\omega) L_{t, NR}$. The firm is indifferent to who it hires. Everyone goes into a big labor pool and recieves identical wage $w_t$. This is where the steady-state blues come in. If we assume a CRRA utility for each household:\n", "\n", "$$ u_{x,t} = \\frac{C_{x,t}^{1 - \\sigma_C}}{1 - \\sigma_C} - \\frac{L_{x,t}^{1 + \\sigma_L}}{1+\\sigma_L} $$\n", "\n", "Dropping the $t$ subscript to denote the steady state, labor supply for each household will be the usual solution:\n", "\n", "$$ C_{x}^{\\sigma_C} L_{x}^{\\sigma_L} = w $$\n", "\n", "Or, solving for $C_{x,t}$ as a function of $L_{x,t}$ :\n", "\n", "$$ C_{x} = w^{\\frac{1}{\\sigma_C}} L_{x}^{-\\frac{\\sigma_L}{\\sigma_C}}$$\n", "\n", "Focusing on the non-Ricardian households, they consume all their income, and their income only comes from labor. So their budget constraint is very simple:\n", "\n", "$$ C_{NR} = w L_{NR} $$\n", "\n", "Plugging in the above expression and solving for $L_{NR}$:\n", "\n", "$$\n", "\\begin{align}\n", "w^{\\frac{1}{\\sigma_C}} L_{NR}^{-\\frac{\\sigma_L}{\\sigma_C}} = w L_{NR} \\\\\n", "L_{NR}^{-\\left ( 1 + \\frac{\\sigma_L}{\\sigma_C} \\right ) } &= w^{1 - \\frac{1}{\\sigma_C}} \\\\\n", "L_{NR}^{-\\frac{\\sigma_C + \\sigma_L}{\\sigma_C}} &= w^{\\frac{\\sigma_C - 1}{\\sigma_L}} \\\\\n", "L_{NR} &= w^{\\frac{1 - \\sigma_C}{\\sigma_C + \\sigma_L}}\n", "\\end{align}\n", "$$\n", "\n", "So that total consumption in the economy is:\n", "\n", "$$ C = \\omega w^{\\frac{1}{\\sigma_C}} L_{R}^{-\\frac{\\sigma_L}{\\sigma_C}} + (1 - \\omega) w^{\\frac{1}{\\sigma_C}} L_{NR}^{-\\frac{\\sigma_L}{\\sigma_C}} $$\n", "\n", "From the law of motion of capital, we know that $I = \\delta K$. Defining $N = \\frac{K}{L}$ as the capital-labor ratio, that's $I = \\delta N L$. We can also re-write the Cobb-Douglas production function as $Y = N^\\alpha L$. From the firm problem we know $N = \\left ( \\frac{\\alpha \\mu}{r} \\right )^{\\frac{1}{1 - \\alpha}}$ So the total resource contraint in the economy will be:\n", "\n", "$$\n", "\\begin{align}\n", "Y &= C + I \\\\\n", "N^\\alpha L &= \\omega w^{\\frac{1}{\\sigma_C}} L_{R}^{-\\frac{\\sigma_L}{\\sigma_C}} + (1 - \\omega) w^{\\frac{1}{\\sigma_C}} L_{NR}^{-\\frac{\\sigma_L}{\\sigma_C}} + \\delta N L \\\\\n", "(N^\\alpha - \\delta N)L &= \\omega w^{\\frac{1}{\\sigma_C}} L_{R}^{-\\frac{\\sigma_L}{\\sigma_C}} + (1 - \\omega) w^{\\frac{1}{\\sigma_C}} L_{NR}^{-\\frac{\\sigma_L}{\\sigma_C}}\n", "\\end{align}\n", "$$\n", "\n", "In the \"usual\" setup, we could divide through and solve for $L$, but here we're stuck, because $L_{NR}$ cannot be trivially isolated. We know $L = \\omega L_R + (1 - \\omega) L_{NR}$, so plug that in and do some algebra:\n", "\n", "$$\n", "\\begin{align}\n", "(N^\\alpha - \\delta N) \\left ( \\omega L_R + (1 - \\omega) L_{NR} \\right ) &= \\omega w^{\\frac{1}{\\sigma_C}} L_{R}^{-\\frac{\\sigma_L}{\\sigma_C}} + (1 - \\omega) w^{\\frac{1}{\\sigma_C}} L_{NR}^{-\\frac{\\sigma_L}{\\sigma_C}} \\\\\n", "(N^\\alpha - \\delta N)\\omega L_R - \\omega w^{\\frac{1}{\\sigma_C}} L_{R}^{-\\frac{\\sigma_L}{\\sigma_C}} &= (1 - \\omega) w^{\\frac{1}{\\sigma_C}} L_{NR}^{-\\frac{\\sigma_L}{\\sigma_C}} - (N^\\alpha - \\delta N) (1 - \\omega) L_{NR}\n", "\\end{align}\n", "$$\n", "\n", "Now we're good and stuck. You can see that the right-hand side is competely known. The left-hand side, however, cannot be further simplifed to isolate and solve for $L_R$. As a result, we need to resort to numerical methods to solve this steady state." ] }, { "cell_type": "markdown", "id": "5c4fb3a8", "metadata": {}, "source": [ "# Load and Solve\n", "\n", "In this model, note that we still provide a `steady_state` block. This contains a *partial* steady state, which has solutions for:\n", "\n", "1. Trivial expressions ($TFP$, $\\zeta_\\beta$, $r$, $mc$)\n", "2. The firm's problem, which does not change\n", "3. Aggregate variables, which also don't change. \n", "\n", "We can actually ignore the household-specific consumption and labor and obtain the usual expressions for aggregate output, investment, consumption, and capital. Since the household variables are omitted, they will be computed numerically." ] }, { "cell_type": "code", "execution_count": 2, "id": "2ee5d32c", "metadata": { "execution": { "iopub.execute_input": "2025-03-15T11:24:22.406354Z", "iopub.status.busy": "2025-03-15T11:24:22.405833Z", "iopub.status.idle": "2025-03-15T11:24:22.827113Z", "shell.execute_reply": "2025-03-15T11:24:22.826874Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "\n", " \n", " \n", "
\\[u_{R t} = - shock_{\\beta R t} \\left(\\frac{C_{R t}^{1 - \\sigma_{C}}}{\\sigma_{C} - 1} + \\frac{L_{R t}^{\\sigma_{L} + 1}}{\\sigma_{L} + 1}\\right)\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ C_{R t}, \\ L_{R t}, \\ I_{t}, \\ K_{t}\\right]\\right)\\]
\n", "\\[U_{R t} = \\beta U_{R t+1} + u_{R t}\\]
\n", "\\[C_{R t} + I_{t} = K_{t-1} r_{t} + L_{R t} w_{t}\\]
\n", "\\[K_{t} = I_{t} - K_{t-1} \\left(\\delta - 1\\right)\\]
\n", "\\[\\log{\\left(shock_{\\beta R t} \\right)} = \\rho_{\\beta R} \\log{\\left(shock_{\\beta R t-1} \\right)} + \\epsilon_{\\beta R t}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ \\epsilon_{\\beta R t}\\right]\\right)\\]
\n", "\\[\\beta = 0.99\\]
\n", "\\[\\delta = 0.02\\]
\n", "\\[\\sigma_{C} = 1.5\\]
\n", "\\[\\sigma_{L} = 2\\]
\n", "\\[\\rho_{\\beta R} = 0.95\\]
\n", "\\[u_{NR t} = - \\frac{C_{NR t}^{1 - \\sigma_{C}}}{\\sigma_{C} - 1} - \\frac{L_{NR t}^{\\sigma_{L} + 1}}{\\sigma_{L} + 1}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ C_{NR t}, \\ L_{NR t}\\right]\\right)\\]
\n", "\\[U_{NR t} = \\beta U_{NR t+1} + u_{NR t}\\]
\n", "\\[C_{NR t} = L_{NR t} w_{t}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ K_{t-1}, \\ L_{t}\\right]\\right)\\]
\n", "\\[TC_{t} = - K_{t-1} r_{t} - L_{t} w_{t}\\]
\n", "\\[Y_{t} = K_{t-1}^{\\alpha} L_{t}^{1 - \\alpha} TFP_{t}\\]
\n", "\\[mc_{t} = 1\\]
\n", "\\[\\log{\\left(TFP_{t} \\right)} = \\rho_{TFP} \\log{\\left(TFP_{t-1} \\right)} + \\epsilon_{TFP t}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ \\epsilon_{TFP t}\\right]\\right)\\]
\n", "\\[\\alpha = 0.35\\]
\n", "\\[\\rho_{TFP} = 0.95\\]
\n", "\\[Y_{t} = C_{t} + I_{t}\\]
\n", "\\[L_{t} = \\omega L_{R t} - L_{NR t} \\left(\\omega - 1\\right)\\]
\n", "\\[C_{t} = \\omega C_{R t} - C_{NR t} \\left(\\omega - 1\\right)\\]
\n", "\\[\\omega = 0.5\\]
\n", "\\[u_{R t} = - shock_{\\beta R t} \\left(\\frac{C_{R t}^{1 - \\sigma_{R}}}{\\sigma_{R} - 1} + L_{R t} \\Theta_{R t}\\right)\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ C_{R t}, \\ L_{R t}, \\ I_{t}, \\ K_{t}\\right]\\right)\\]
\n", "\\[U_{R t} = \\beta U_{R t+1} + u_{R t}\\]
\n", "\\[C_{R t} + I_{t} = K_{t-1} r_{t} + L_{R t} w_{t}\\]
\n", "\\[K_{t} = I_{t} - K_{t-1} \\left(\\delta - 1\\right)\\]
\n", "\\[\\log{\\left(shock_{\\beta R t} \\right)} = \\rho_{\\beta R} \\log{\\left(shock_{\\beta R t-1} \\right)} + \\epsilon_{\\beta R t}\\]
\n", "\\[\\log{\\left(\\Theta_{R t} \\right)} = \\rho_{\\Theta R} \\log{\\left(\\Theta_{R t-1} \\right)} + \\epsilon_{\\Theta R t} - \\left(\\rho_{\\Theta R} - 1\\right) \\log{\\left(\\Theta_{R} \\right)}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ \\epsilon_{\\beta R t}, \\ \\epsilon_{\\Theta R t}\\right]\\right)\\]
\n", "\\[\\beta = 0.99\\]
\n", "\\[\\delta = 0.02\\]
\n", "\\[\\sigma_{R} = 1.5\\]
\n", "\\[\\Theta_{R} = 1.0\\]
\n", "\\[\\rho_{\\beta R} = 0.95\\]
\n", "\\[\\rho_{\\Theta R} = 0.95\\]
\n", "\\[u_{NR t} = - \\frac{C_{NR t}^{1 - \\sigma_{N}}}{\\sigma_{N} - 1} - L_{NR t} \\Theta_{N t}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ C_{NR t}, \\ L_{NR t}\\right]\\right)\\]
\n", "\\[U_{NR t} = \\beta U_{NR t+1} + u_{NR t}\\]
\n", "\\[C_{NR t} = L_{NR t} w_{t}\\]
\n", "\\[\\log{\\left(\\Theta_{N t} \\right)} = \\rho_{\\Theta N} \\log{\\left(\\Theta_{N t-1} \\right)} + \\epsilon_{\\Theta N t} - \\left(\\rho_{\\Theta N} - 1\\right) \\log{\\left(\\Theta_{N} \\right)}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ \\epsilon_{\\Theta N t}\\right]\\right)\\]
\n", "\\[\\Theta_{N} = 1.0\\]
\n", "\\[\\sigma_{N} = 1.5\\]
\n", "\\[\\rho_{\\Theta N} = 0.95\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ K_{t-1}, \\ L_{t}\\right]\\right)\\]
\n", "\\[TC_{t} = - K_{t-1} r_{t} - L_{t} w_{t}\\]
\n", "\\[Y_{t} = K_{t-1}^{\\alpha} L_{t}^{1 - \\alpha} TFP_{t}\\]
\n", "\\[mc_{t} = 1\\]
\n", "\\[\\log{\\left(TFP_{t} \\right)} = \\rho_{TFP} \\log{\\left(TFP_{t-1} \\right)} + \\epsilon_{TFP t}\\]
\n", "\\[\\operatorname{Set}\\left(\\left[ \\epsilon_{TFP t}\\right]\\right)\\]
\n", "\\[\\alpha = 0.35\\]
\n", "\\[\\rho_{TFP} = 0.95\\]
\n", "\\[Y_{t} = C_{t} + I_{t}\\]
\n", "\\[L_{t} = \\omega L_{R t} - L_{NR t} \\left(\\omega - 1\\right)\\]
\n", "\\[C_{t} = \\omega C_{R t} - C_{NR t} \\left(\\omega - 1\\right)\\]
\n", "\\[\\omega = 0.66\\]
\n", "Model Requirements \n", " \n", " Variable Shape Constraints Dimensions \n", " ──────────────────────────────────────────────────────────────────────────── \n", " Theta_N () None \n", " Theta_R () None \n", " alpha () None \n", " beta () None \n", " delta () None \n", " omega () None \n", " rho_TFP () None \n", " rho_Theta_N () None \n", " rho_Theta_R () None \n", " rho_beta_R () None \n", " sigma_N () None \n", " sigma_R () None \n", " state_cov (4, 4) Positive Semi-Definite ('shock', 'shock_aux') \n", " error_sigma_Y () Positive None \n", " error_sigma_C () Positive None \n", " error_sigma_C_R () Positive None \n", " error_sigma_L () Positive None \n", " error_sigma_L_R () Positive None \n", " error_sigma_w () Positive None \n", " \n", " These parameters should be assigned priors inside a PyMC model block before \n", " calling the build_statespace_graph method. \n", "\n" ], "text/plain": [ "\u001b[3m Model Requirements \u001b[0m\n", " \n", " \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m Dimensions\u001b[0m\u001b[1m \u001b[0m \n", " ──────────────────────────────────────────────────────────────────────────── \n", " Theta_N \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " Theta_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " alpha \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " beta \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " delta \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " omega \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " rho_TFP \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " rho_Theta_N \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " rho_Theta_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " rho_beta_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " sigma_N \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " sigma_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " state_cov \u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m, \u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m Positive Semi-Definite \u001b[1m(\u001b[0m\u001b[32m'shock'\u001b[0m, \u001b[32m'shock_aux'\u001b[0m\u001b[1m)\u001b[0m \n", " error_sigma_Y \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n", " error_sigma_C \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n", " error_sigma_C_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n", " error_sigma_L \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n", " error_sigma_L_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n", " error_sigma_w \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n", " \n", "\u001b[2;3m These parameters should be assigned priors inside a PyMC model block before \u001b[0m\n", "\u001b[2;3m calling the build_statespace_graph method. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ss_mod.configure(\n", " observed_states=[\"Y\", \"C\", \"C_R\", \"L\", \"L_R\", \"w\"],\n", " measurement_error=[\"Y\", \"C\", \"C_R\", \"L\", \"L_R\", \"w\"],\n", " full_shock_covaraince=True,\n", " solver=\"scan_cycle_reduction\",\n", " mode=\"JAX\",\n", " max_iter=20,\n", " use_adjoint_gradients=True,\n", ")" ] }, { "cell_type": "markdown", "id": "4773e15c", "metadata": {}, "source": [ "## Model definition\n", "\n", "This model has 4 shocks. It can be interesting, but difficult, to learn about covariances between shocks. One way to learn these covariances is to define a prior over *covariance matrices*. This can be done in PyMC using an `LKJCholeskyCov` prior. This prior has a parameter $\\eta$ that controls shrinkage towards diagonal matrices by making the correlation parmeters more and more concentrated arond zero. So, the higher is $\\eta$, the more diagonal your matrices will be. When $\\eta = 1$, the correlations are uniform between -1 and 1." ] }, { "cell_type": "code", "execution_count": 46, "id": "a570e8d1", "metadata": { "execution": { "iopub.execute_input": "2025-03-15T11:24:49.355393Z", "iopub.status.busy": "2025-03-15T11:24:49.355327Z", "iopub.status.idle": "2025-03-15T11:24:49.407344Z", "shell.execute_reply": "2025-03-15T11:24:49.407067Z" } }, "outputs": [], "source": [ "all_priors = ss_mod.param_priors | ss_mod.shock_priors\n", "\n", "with pm.Model(coords=ss_mod.coords) as pm_mod:\n", " ss_mod.to_pymc()\n", " for var_name in ss_mod.error_states:\n", " x = pz.maxent(pz.Gamma(), lower=0.01, upper=0.05, plot=False)\n", " all_priors[f\"error_sigma_{var_name}\"] = x\n", " x.to_pymc(name=f\"error_sigma_{var_name}\")\n", "\n", " chol, *_ = pm.LKJCholeskyCov(\"state_chol\", n=4, eta=6, sd_dist=pm.HalfNormal.dist(sigma=0.05))\n", " cov = pm.Deterministic(\"state_cov\", chol @ chol.T, dims=[\"shock\", \"shock_aux\"])" ] }, { "cell_type": "markdown", "id": "8a9259f0", "metadata": {}, "source": [ "## Generate data using draws from the prior" ] }, { "cell_type": "code", "execution_count": 47, "id": "cad08c0a", "metadata": { "execution": { "iopub.execute_input": "2025-03-15T11:24:49.408706Z", "iopub.status.busy": "2025-03-15T11:24:49.408618Z", "iopub.status.idle": "2025-03-15T11:24:58.913199Z", "shell.execute_reply": "2025-03-15T11:24:58.912929Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jessegrabowski/mambaforge/envs/geconpy-dev/lib/python3.12/site-packages/pymc_extras/statespace/utils/data_tools.py:175: ImputationWarning: Provided data contains missing values and will be automatically imputed as hidden states during Kalman filtering.\n", " warnings.warn(impute_message, ImputationWarning)\n", "Sampling: [Theta_N, Theta_R, alpha, beta, delta, error_sigma_C, error_sigma_C_R, error_sigma_L, error_sigma_L_R, error_sigma_Y, error_sigma_w, obs, omega, rho_TFP, rho_Theta_N, rho_Theta_R, rho_beta_R, sigma_N, sigma_R, state_chol]\n", "Sampling: [prior_combined]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ef3a3356088a4d9b99d7a8e4ddfaaa07", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "true_params, data, prior = ge.data_from_prior(ss_mod, pm_mod, random_seed=rng)" ] }, { "cell_type": "code", "execution_count": 48, "id": "892ccdd2", "metadata": { "execution": { "iopub.execute_input": "2025-03-15T11:24:58.923195Z", "iopub.status.busy": "2025-03-15T11:24:58.923127Z", "iopub.status.idle": "2025-03-15T11:24:59.494982Z", "shell.execute_reply": "2025-03-15T11:24:59.494734Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
Sampler Progress
\n", "Total Chains: 4
\n", "Active Chains: 0
\n", "\n", " Finished Chains:\n", " 4\n", "
\n", "Sampling for 6 minutes
\n", "\n", " Estimated Time to Completion:\n", " now\n", "
\n", "\n", " \n", "| Progress | \n", "Draws | \n", "Divergences | \n", "Step Size | \n", "Gradients/Draw | \n", "
|---|---|---|---|---|
| \n", " \n", " | \n", "2000 | \n", "24 | \n", "0.32 | \n", "15 | \n", "
| \n", " \n", " | \n", "2000 | \n", "0 | \n", "0.32 | \n", "15 | \n", "
| \n", " \n", " | \n", "2000 | \n", "0 | \n", "0.34 | \n", "15 | \n", "
| \n", " \n", " | \n", "2000 | \n", "3 | \n", "0.33 | \n", "31 | \n", "
| \n", " | mean | \n", "sd | \n", "hdi_3% | \n", "hdi_97% | \n", "mcse_mean | \n", "mcse_sd | \n", "ess_bulk | \n", "ess_tail | \n", "r_hat | \n", "
|---|---|---|---|---|---|---|---|---|---|
| Theta_N | \n", "3.096 | \n", "1.992 | \n", "0.224 | \n", "6.847 | \n", "0.060 | \n", "0.036 | \n", "1039.0 | \n", "2136.0 | \n", "1.00 | \n", "
| Theta_R | \n", "1.767 | \n", "1.417 | \n", "0.029 | \n", "4.343 | \n", "0.085 | \n", "0.052 | \n", "266.0 | \n", "375.0 | \n", "1.02 | \n", "
| alpha | \n", "0.431 | \n", "0.035 | \n", "0.366 | \n", "0.495 | \n", "0.001 | \n", "0.001 | \n", "2054.0 | \n", "2595.0 | \n", "1.02 | \n", "
| beta | \n", "0.913 | \n", "0.016 | \n", "0.885 | \n", "0.945 | \n", "0.002 | \n", "0.002 | \n", "58.0 | \n", "38.0 | \n", "1.06 | \n", "
| delta | \n", "0.039 | \n", "0.011 | \n", "0.019 | \n", "0.060 | \n", "0.001 | \n", "0.001 | \n", "67.0 | \n", "157.0 | \n", "1.05 | \n", "
| omega | \n", "0.911 | \n", "0.056 | \n", "0.809 | \n", "0.984 | \n", "0.003 | \n", "0.002 | \n", "159.0 | \n", "784.0 | \n", "1.03 | \n", "
| rho_TFP | \n", "0.909 | \n", "0.026 | \n", "0.864 | \n", "0.958 | \n", "0.001 | \n", "0.000 | \n", "1705.0 | \n", "2493.0 | \n", "1.00 | \n", "
| rho_Theta_N | \n", "0.738 | \n", "0.161 | \n", "0.442 | \n", "0.987 | \n", "0.003 | \n", "0.003 | \n", "2970.0 | \n", "1944.0 | \n", "1.01 | \n", "
| rho_Theta_R | \n", "0.615 | \n", "0.054 | \n", "0.518 | \n", "0.715 | \n", "0.001 | \n", "0.001 | \n", "1944.0 | \n", "2545.0 | \n", "1.00 | \n", "
| rho_beta_R | \n", "0.798 | \n", "0.153 | \n", "0.492 | \n", "0.990 | \n", "0.024 | \n", "0.017 | \n", "63.0 | \n", "70.0 | \n", "1.05 | \n", "
| sigma_N | \n", "2.325 | \n", "0.578 | \n", "1.431 | \n", "3.550 | \n", "0.085 | \n", "0.079 | \n", "76.0 | \n", "44.0 | \n", "1.06 | \n", "
| sigma_R | \n", "1.584 | \n", "0.138 | \n", "1.344 | \n", "1.863 | \n", "0.016 | \n", "0.011 | \n", "72.0 | \n", "31.0 | \n", "1.05 | \n", "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 695/695 0.00002214 0.00000000 33.71351371 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m695/695 \u001b[0m 0.00002214 0.00000000 33.71351371 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "0992f7814916495da59448934fad14f8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "13de64fa842245eba5266ded257f31d9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c68357e1bcb24173b147e1b8a3ad8846", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 773/773 0.00000216 0.00000000 33.85727195 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m773/773 \u001b[0m 0.00000216 0.00000000 33.85727195 \n \n" }, "metadata": {}, "output_type": "display_data" } ], 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\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 613/613 0.00001539 0.00000000 33.74272831 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m613/613 \u001b[0m 0.00001539 0.00000000 33.74272831 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "36f4a99120194be68b26c1bf155e3164": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e44e19ad0c4a46159f9f9c506812d44c", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 729/729 0.00000021 0.00000000 34.00247852 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m729/729 \u001b[0m 0.00000021 0.00000000 34.00247852 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "40948fc81f8647ffb024d5aa78aac208": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "466268c123ea46a9a405cd0f828b2788": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "46bccb4fcd43461ca87b55219e922252": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_d31eb41c1aa64856bd22d2b3211c92b9", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 600/600 0.00004690 0.00000000 33.62343094 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m600/600 \u001b[0m 0.00004690 0.00000000 33.62343094 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "5693ad30fcf8445abb689af540e26937": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "600b6e615ba84b18aff16d87ddc7ada6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c63acbf7debb4e3880723b8c0958577f", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 908/908 0.00032835 0.00000000 33.05617962 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m908/908 \u001b[0m 0.00032835 0.00000000 33.05617962 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "6264cefc31a044dd858851a0a6fe595e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7173a74335d5491cb31d8d3ae2305c30", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 751/751 0.00003202 0.00000000 33.67548654 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m751/751 \u001b[0m 0.00003202 0.00000000 33.67548654 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "6523d419731848f5b155ff15c93417e4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e3e296714c9345da9cc54d1dfdeb3823", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 759/759 0.00007033 0.00000000 33.54976761 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m759/759 \u001b[0m 0.00007033 0.00000000 33.54976761 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "69b2f6c2e2a647daaafa395b5d29b017": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "703ae3666811445b8359bc3b1dfed196": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_149149a89cc5449699b0d0ee7d587dbc", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 702/702 0.00000002 0.00000000 34.11400506 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m702/702 \u001b[0m 0.00000002 0.00000000 34.11400506 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "7173a74335d5491cb31d8d3ae2305c30": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "73d9e23e0d5447458e5dda74da304d82": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7db81dd9e3c444318099df5c70214f39": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_73d9e23e0d5447458e5dda74da304d82", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 662/662 0.00000008 0.00000000 34.05434932 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m662/662 \u001b[0m 0.00000008 0.00000000 34.05434932 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "7f50440b586d40628ded86fcce0626d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7fc6503512564bf5ae34bce6993f60fc": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_fa04f9daa59744169ab6ea6f892a7d45", "msg_id": "", "outputs": [ { "data": { "text/html": "
Sampling ... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 / 0:00:01\n\n", "text/plain": "Sampling ... \u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m 0:00:00 / 0:00:01\n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "81eb363b3be14032b70b9f09d236d2f9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_69b2f6c2e2a647daaafa395b5d29b017", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 784/784 0.00001068 0.00000000 33.76682290 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m784/784 \u001b[0m 0.00001068 0.00000000 33.76682290 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "821842af98e64f249e1b5611313feadb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "956c828ca91a45a780cab368199f176a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_19337e06e028487f9fbde477d6c7e359", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 545/545 0.00000080 0.00000000 33.91903623 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m545/545 \u001b[0m 0.00000080 0.00000000 33.91903623 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "958139918b06480bad7fa9e43a6540b5": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7f50440b586d40628ded86fcce0626d4", "msg_id": "", "outputs": [ { "data": { "text/html": "
Sampling ... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 / 0:00:01\n\n", "text/plain": "Sampling ... \u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m 0:00:00 / 0:00:01\n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "a07d139f0ee5412e8cbe43f73fef64c2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_821842af98e64f249e1b5611313feadb", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 138/138 0.00000333 0.00000000 33.83227275 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m138/138 \u001b[0m 0.00000333 0.00000000 33.83227275 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "a27d66a683364741ba890f41c6fdbf03": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0992f7814916495da59448934fad14f8", "msg_id": "", "outputs": [ { "data": { "text/html": "
Sampling ... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 / 0:02:11\n\n", "text/plain": "Sampling ... \u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m 0:00:00 / 0:02:11\n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "a28cef837b4a4d8292973f0641719d8f": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_ed186f4559cb44a8b501c7e37d74bf2f", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 803/803 0.00000736 0.00000000 33.78849424 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m803/803 \u001b[0m 0.00000736 0.00000000 33.78849424 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "a61db1eae869422fa559dddac71e6a80": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a7aa7feb357d4e78bbac6fd352db575f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ad29ed10803348bab55740fb406bfb98": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_40948fc81f8647ffb024d5aa78aac208", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 700/700 0.00000009 0.00000000 34.05127032 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m700/700 \u001b[0m 0.00000009 0.00000000 34.05127032 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "b4648f17af2e471090d4c93f63bed822": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_e807c9b06fd244728b5b6198073c97b7", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 612/612 0.00000135 0.00000000 33.88587280 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m612/612 \u001b[0m 0.00000135 0.00000000 33.88587280 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "b9a845c1bee845e49bceb279de65a492": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_a61db1eae869422fa559dddac71e6a80", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 613/613 0.00000888 0.00000000 33.77782170 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m613/613 \u001b[0m 0.00000888 0.00000000 33.77782170 \n \n" }, "metadata": {}, "output_type": "display_data" } ], 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\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 187/187 0.00018101 0.00000000 33.28686843 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m187/187 \u001b[0m 0.00018101 0.00000000 33.28686843 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "d08503e3e1cf402d9adec43ebf471ec2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_466268c123ea46a9a405cd0f828b2788", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 705/705 0.00010954 0.00000000 33.44322340 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m705/705 \u001b[0m 0.00010954 0.00000000 33.44322340 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "d31eb41c1aa64856bd22d2b3211c92b9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d3c4dcb6694c40c59121df9c1af8133e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_c0c0d2ef7a774383a903d84b2404f92b", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 473/473 0.00000044 0.00000000 33.95763360 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m473/473 \u001b[0m 0.00000044 0.00000000 33.95763360 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "dbf052b97c9a488da43b4d742dfa52c4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5693ad30fcf8445abb689af540e26937", "msg_id": "", "outputs": [ { "data": { "text/html": "
\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 866/866 0.00069368 0.00000000 32.72366190 \n \n\n", "text/plain": " \n \u001b[1m \u001b[0m\u001b[1mMinimizing \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mElapsed\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mObjective \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||grad|| \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m||hess|| \u001b[0m\u001b[1m \u001b[0m \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[33m0:00:00\u001b[0m \u001b[32m866/866 \u001b[0m 0.00069368 0.00000000 32.72366190 \n \n" }, "metadata": {}, "output_type": "display_data" } ], 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