{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "931b360a", "metadata": { "execution": { "iopub.execute_input": "2025-03-15T11:24:20.838153Z", "iopub.status.busy": "2025-03-15T11:24:20.837716Z", "iopub.status.idle": "2025-03-15T11:24:20.843651Z", "shell.execute_reply": "2025-03-15T11:24:20.843136Z" } }, "outputs": [], "source": [ "import os\n", "\n", "N_THREADS = \"4\"\n", "os.environ[\"OMP_NUM_THREADS\"] = N_THREADS\n", "os.environ[\"OPENBLAS_NUM_THREADS\"] = N_THREADS\n", "os.environ[\"MKL_NUM_THREADS\"] = N_THREADS\n", "os.environ[\"VECLIB_MAXIMUM_THREADS\"] = N_THREADS\n", "os.environ[\"NUMEXPR_NUM_THREADS\"] = N_THREADS" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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", "
\\[TFP_{ss} = 1.0\\]
\n", "\\[shock_{\\beta R ss} = 1.0\\]
\n", "\\[r_{ss} = \\delta - 1 + \\frac{1}{\\beta}\\]
\n", "\\[w_{ss} = - \\left(\\frac{\\alpha}{r_{ss}}\\right)^{- \\frac{\\alpha}{\\alpha - 1}} \\left(\\alpha - 1\\right)\\]
\n", "\\[mc_{ss} = 1\\]
\n", "\\[Y_{ss} = \\left(w_{ss} \\left(- \\frac{w_{ss}}{\\alpha - 1}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}} \\left(- \\frac{r_{ss}}{\\alpha \\delta - r_{ss}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}}\\]
\n", "\\[I_{ss} = \\frac{\\alpha \\delta Y_{ss}}{r_{ss}}\\]
\n", "\\[C_{ss} = Y_{ss}^{\\frac{\\left(-1\\right) \\sigma_{L}}{\\sigma_{C}}} \\left(w_{ss}^{\\sigma_{L} + 1} \\left(1 - \\alpha\\right)^{- \\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C}}}\\]
\n", "\\[K_{ss} = \\frac{\\alpha Y_{ss} mc_{ss}}{r_{ss}}\\]
\n", "\\[L_{ss} = - \\frac{Y_{ss} mc_{ss} \\left(\\alpha - 1\\right)}{w_{ss}}\\]
\n", "\\[TC_{ss} = - K_{ss} r_{ss} - L_{ss} w_{ss}\\]
\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} = 2.0\\]
\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", "\\[N_{ss} = \\left(\\frac{\\alpha TFP_{ss}}{r_{ss}}\\right)^{- \\frac{1}{\\alpha - 1}}\\]
\n", "\\[TFP_{ss} = 1.0\\]
\n", "\\[shock_{\\beta R ss} = 1.0\\]
\n", "\\[\\Theta_{R ss} = \\Theta_{R}\\]
\n", "\\[\\Theta_{N ss} = \\Theta_{N}\\]
\n", "\\[r_{ss} = \\delta - 1 + \\frac{1}{\\beta}\\]
\n", "\\[w_{ss} = - N_{ss}^{\\alpha} \\left(\\alpha - 1\\right)\\]
\n", "\\[C_{R ss} = \\left(\\frac{w_{ss}}{\\Theta_{R}}\\right)^{\\frac{1}{\\sigma_{R}}}\\]
\n", "\\[C_{NR ss} = \\left(\\frac{w_{ss}}{\\Theta_{N}}\\right)^{\\frac{1}{\\sigma_{N}}}\\]
\n", "\\[C_{ss} = \\omega C_{R ss} - C_{NR ss} \\left(\\omega - 1\\right)\\]
\n", "\\[L_{ss} = - \\frac{C_{ss}}{\\delta N_{ss} - N_{ss}^{\\alpha}}\\]
\n", "\\[L_{NR ss} = \\frac{C_{NR ss}}{w_{ss}}\\]
\n", "\\[L_{R ss} = - \\frac{- L_{NR ss} \\left(\\omega - 1\\right) - L_{ss}}{\\omega}\\]
\n", "\\[K_{ss} = L_{ss} N_{ss}\\]
\n", "\\[I_{ss} = \\delta K_{ss}\\]
\n", "\\[Y_{ss} = C_{ss} + I_{ss}\\]
\n", "\\[\\lambda_{R ss} = C_{R ss}^{- \\sigma_{R}}\\]
\n", "\\[\\lambda_{NR ss} = C_{NR ss}^{- \\sigma_{N}}\\]
\n", "\\[q_{ss} = \\lambda_{R ss}\\]
\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 () Positive None \n", " sigma_R () Positive None \n", " state_cov (4, 4) Positive Semi-Definite ('shock', 'shock_aux') \n", " error_sigma_Y () None \n", " error_sigma_C () None \n", " error_sigma_C_R () None \n", " error_sigma_L () None \n", " error_sigma_L_R () None \n", " error_sigma_w () 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 Positive \u001b[3;35mNone\u001b[0m \n", " sigma_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \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 \u001b[3;35mNone\u001b[0m \n", " error_sigma_C \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " error_sigma_C_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " error_sigma_L \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " error_sigma_L_R \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \u001b[3;35mNone\u001b[0m \n", " error_sigma_w \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m \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": 40, "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": 41, "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:159: ImputationWarning: Provided data contains missing values and will be automatically imputed as hidden states during Kalman filtering.\n", " warnings.warn(impute_message, ImputationWarning)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [prior_combined]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "958139918b06480bad7fa9e43a6540b5", "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": 42, "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: 6
\n", "Active Chains: 0
\n", "\n", " Finished Chains:\n", " 6\n", "
\n", "Sampling for 12 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", "1500 | \n", "6 | \n", "0.55 | \n", "7 | \n", "
| \n", " \n", " | \n", "1500 | \n", "2 | \n", "0.56 | \n", "7 | \n", "
| \n", " \n", " | \n", "1500 | \n", "1 | \n", "0.55 | \n", "7 | \n", "
| \n", " \n", " | \n", "1500 | \n", "0 | \n", "0.57 | \n", "7 | \n", "
| \n", " \n", " | \n", "1500 | \n", "3 | \n", "0.60 | \n", "5 | \n", "
| \n", " \n", " | \n", "1500 | \n", "5 | \n", "0.58 | \n", "7 | \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", "2.283 | \n", "1.434 | \n", "0.214 | \n", "4.931 | \n", "0.022 | \n", "0.018 | \n", "4855.0 | \n", "2272.0 | \n", "1.00 | \n", "
| Theta_R | \n", "2.326 | \n", "1.402 | \n", "0.371 | \n", "4.902 | \n", "0.021 | \n", "0.019 | \n", "5753.0 | \n", "2043.0 | \n", "1.00 | \n", "
| alpha | \n", "0.369 | \n", "0.057 | \n", "0.267 | \n", "0.481 | \n", "0.001 | \n", "0.001 | \n", "4408.0 | \n", "2276.0 | \n", "1.00 | \n", "
| beta | \n", "0.962 | \n", "0.011 | \n", "0.941 | \n", "0.981 | \n", "0.000 | \n", "0.000 | \n", "4006.0 | \n", "1789.0 | \n", "1.00 | \n", "
| delta | \n", "0.030 | \n", "0.008 | \n", "0.016 | \n", "0.045 | \n", "0.000 | \n", "0.000 | \n", "4408.0 | \n", "2368.0 | \n", "1.00 | \n", "
| omega | \n", "0.806 | \n", "0.069 | \n", "0.679 | \n", "0.928 | \n", "0.001 | \n", "0.001 | \n", "5009.0 | \n", "2223.0 | \n", "1.01 | \n", "
| rho_TFP | \n", "0.919 | \n", "0.028 | \n", "0.865 | \n", "0.966 | \n", "0.000 | \n", "0.000 | \n", "4890.0 | \n", "2313.0 | \n", "1.00 | \n", "
| rho_Theta_N | \n", "0.788 | \n", "0.122 | \n", "0.575 | \n", "0.987 | \n", "0.002 | \n", "0.001 | \n", "4184.0 | \n", "2220.0 | \n", "1.00 | \n", "
| rho_Theta_R | \n", "0.523 | \n", "0.141 | \n", "0.261 | \n", "0.787 | \n", "0.002 | \n", "0.002 | \n", "4002.0 | \n", "2234.0 | \n", "1.00 | \n", "
| rho_beta_R | \n", "0.785 | \n", "0.079 | \n", "0.641 | \n", "0.929 | \n", "0.001 | \n", "0.001 | \n", "3928.0 | \n", "1899.0 | \n", "1.00 | \n", "
| sigma_N | \n", "2.330 | \n", "0.336 | \n", "1.766 | \n", "2.987 | \n", "0.005 | \n", "0.004 | \n", "4525.0 | \n", "2364.0 | \n", "1.00 | \n", "
| sigma_R | \n", "1.671 | \n", "0.055 | \n", "1.561 | \n", "1.770 | \n", "0.001 | \n", "0.001 | \n", "4940.0 | \n", "2301.0 | \n", "1.00 | \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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\n Minimizing Elapsed Iteration Objective ||grad|| ||hess|| \n ────────────────────────────────────────────────────────────────────────────────────────────────── \n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00:00 756/756 0.00000500 0.00000000 33.80977920 \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[32m756/756 \u001b[0m 0.00000500 0.00000000 33.80977920 \n \n" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "ed186f4559cb44a8b501c7e37d74bf2f": { "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 } }, "f14d561a151346bf92fbe3cbf0466f5c": { "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 } }, "fa04f9daa59744169ab6ea6f892a7d45": { "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 } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }