{ "cells": [ { "cell_type": "markdown", "id": "dad71f89", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "id": "6ab99e77", "metadata": { "load": "myst_code_init.py", "tags": [ "remove-cell" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading pyMOR defaults from file /builds/pymor/pymor/docs/source/pymor_defaults.py\n" ] } ], "source": [ "import warnings\n", "\n", "import matplotlib as mpl\n", "from IPython import get_ipython\n", "\n", "import pymor.tools.random\n", "\n", "ip = get_ipython()\n", "if ip is not None:\n", " ip.run_line_magic('matplotlib', 'inline')\n", "\n", "warnings.filterwarnings('ignore', category=UserWarning, module='torch')\n", "\n", "pymor.tools.random._default_random_state = None\n", "\n", "mpl.rcParams['figure.facecolor'] = (1.0, 1.0, 1.0, 0.0)\n" ] }, { "cell_type": "markdown", "id": "e3a52d19", "metadata": {}, "source": [ "# Tutorial: Model order reduction for port-Hamiltonian systems\n", "\n", "In the [first section](#port-hamiltonian-lti-systems), we introduce the class of\n", "port-Hamiltonian systems and their relationship to two other system-theoretic properties called\n", "*passivity* and *positive realness*. After introducing a toy example in\n", "the [second section](#a-toy-problem-mass-spring-damper-chain), we\n", "look into structure-preserving model reduction schemes for port-Hamiltonian systems\n", "in the [third section](#structure-preserving-model-order-reduction).\n", "\n", "## Port-Hamiltonian LTI systems\n", "\n", "Port-Hamiltonian systems have several favorable properties for modeling, control and\n", "simulation, for example, composability and stability. Furthermore, they adhere to a\n", "power balance equation. Port-Hamiltonian systems are especially suited for\n", "network-based modeling and problems involving multi-physics phenomena. We refer to {cite}`MU23`\n", "for a general introduction to port-Hamiltonian descriptor systems and their applications.\n", "\n", "We say a LTI system is *port-Hamiltonian* if it can be expressed as\n", "\n", "```{math}\n", "E \\dot{x}(t) & = (J - R) Q x(t) + (G-P) u(t), \\\\\n", "y(t) & = (G+P)^T Q x(t) + (S-N) u(t),\n", "```\n", "\n", "with {math}`H := Q^T E`, and if the structure matrix\n", "\n", "```{math}\n", "\\Gamma :=\n", "\\begin{bmatrix}\n", " J & G \\\\\n", " -G^T & N\n", "\\end{bmatrix}\n", "```\n", "\n", "and the dissipation matrix\n", "\n", "```{math}\n", "\\mathcal{W} :=\n", "\\begin{bmatrix}\n", " R & P \\\\\n", " P^T & S\n", "\\end{bmatrix}\n", "```\n", "\n", "satisfy\n", "{math}`H = H^T \\succ 0`,\n", "{math}`\\Gamma^T = -\\Gamma`, and\n", "{math}`\\mathcal{W} = \\mathcal{W}^T \\succcurlyeq 0`.\n", "\n", "The quadratic (energy) function {math}`\\mathcal{H}(x) := \\tfrac{1}{2} x^T H x`,\n", "typically called Hamiltonian, corresponds to the energy stored in the system. In\n", "applications, {math}`E` and/or {math}`Q` often are identity matrices.\n", "\n", "It is known that if the LTI system is minimal and stable, the following are equivalent:\n", "\n", "- The system is passive.\n", "- The system is port-Hamiltonian.\n", "- The system is positive real.\n", "\n", "See for example {cite}`BU22` for more details.\n", "\n", "In pyMOR, there exists a `PHLTIModel` class. Currently, pyMOR only supports\n", "port-Hamiltonian systems with nonsingular E. `PHLTIModel` inherits from\n", "`LTIModel`, so `PHLTIModel` can be used with all reductors that expect\n", "an `LTIModel`. For model reduction, it is often desirable to preserve the\n", "port-Hamiltonian structure, i.e., to compute a ROM that is also port-Hamiltonian.\n", "\n", "If desired, a passive `LTIModel` can be converted into a `PHLTIModel` using\n", "the `from_passive_LTIModel` method.\n", "Consequentely, one option to preserve port-Hamiltonian structure is to use a reductor\n", "that preserves passivity (but returns a ROM of type `LTIModel`) and convert the\n", "ROM into a `PHLTIModel` in a post-processing step.\n", "\n", "## A toy problem: Mass-spring-damper chain\n", "\n", "As a toy problem, we use a mass-spring-damper chain, which can be formulated\n", "as a port-Hamiltonian system (see {cite}`GPBV12`):\n", "\n", "```{image} msd_example.svg\n", ":alt: MSD example\n", ":width: 60%\n", "```\n", "\n", "Here, the spring constants are denoted by {math}`k_i` and the damping constants by\n", "{math}`c_i`, {math}`i=1,\\dots,n/2`.\n", "The inputs {math}`u_1` and {math}`u_2` are the external forces on the first two\n", "masses {math}`m_1` and {math}`m_2`. The system outputs {math}`y_1` and {math}`y_2`\n", "correspond to the velocities of the first two masses {math}`m_1` and {math}`m_2`.\n", "The toy problem is included in pyMOR in the {mod}`pymor.models.examples` module as\n", "`msd_example`.\n", "\n", "## Structure-preserving model order reduction\n", "\n", "pyMOR provides three reductors which can be used for model order reduction\n", "while preserving the port-Hamiltonian structure:\n", "\n", "- pH-IRKA (`PHIRKAReductor`) {cite}`GPBV12`,\n", "- PRBT (`PRBTReductor`) {cite}`DP84,GA04,HJS94`,\n", "- passivity preserving model reduction via spectral factorization\n", " (`SpectralFactorReductor`) {cite}`BU22`.\n", "\n", "In this section, we apply all three reductors on our toy example and compare\n", "their performance. All three reductors are described in {cite}`BU22` in more detail.\n", "\n", "Note: Currently, the `PRBTReductor` and\n", "`SpectralFactorReductor` reductors require\n", "the symmetric part of {math}`D` (i.e., the $S$ matrix in the port-Hamiltonian system)\n", "to be nonsingular. The MSD example has a zero {math}`D` matrix. Therefore,\n", "we have to add a small regularization feedthrough term, i.e., we replace $D$ with\n", "$D+\\varepsilon I_m$. This is a limitation of the current implementation since the\n", "numerical solution of the KYP-LMI is obtained by solving a related Riccati\n", "equation, for instance\n", "\n", "```{math}\n", "A^T X E + E^T X A+ (C^T - E^T X B) (D + D^T)^{-1} (C - B^T X E) = 0,\n", "```\n", "\n", "which is only possible if $D + D^\\top$ is nonsingular.\n", "For `from_passive_LTIModel`, $D + D^\\top$ must be\n", "nonsingular for the same reasons." ] }, { "cell_type": "code", "execution_count": 2, "id": "fc0d0a25", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from pymor.models.examples import msd_example\n", "\n", "fom = msd_example(50, 2)\n", "\n", "# tolerance for solving the Riccati equation instead of KYP-LMI\n", "# by introducing a regularization feedthrough term D\n", "# (required for PRBTReductor and SpectralFactorReductor reductors)\n", "S = fom.S.matrix.copy()\n", "S += np.eye(S.shape[0]) * 1e-12\n", "\n", "fom = fom.with_(S=fom.S.with_(matrix=S),\n", " solver_options={'ricc_pos_lrcf': 'slycot'})" ] }, { "cell_type": "markdown", "id": "cd770421", "metadata": {}, "source": [ "The `ricc_pos_lrcf` solver option refers to the solver used for the underlying\n", "Riccati equation relevant for `PRBTReductor` and\n", "`SpectralFactorReductor`. Possible choices are\n", "`scipy` or `slycot` (if installed). Currently, we recommend `slycot`, since `scipy`\n", "gets into trouble if the associated Hamiltonian pencil has eigenvalues close to the\n", "imaginary axis.\n", "\n", "### pH-IRKA\n", "\n", "The pH-IRKA reductor `PHIRKAReductor` directly returns\n", "a ROM of type `PHLTIModel`. pH-IRKA works similar to the standard IRKA reductor\n", "`IRKAReductor` but with fewer degrees of freedom to preserve\n", "the port-Hamiltonian structure. In more detail, the IRKA fixed-point iteration is performed,\n", "but the left projection matrix is chosen as $W = QV$, which then automatically yields a\n", "reduced pH system with $\\hat{Q} = I_r$." ] }, { "cell_type": "code", "execution_count": 3, "id": "d33f0444", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f8994ef8eee1407981c5a724cfb8f0a2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', width='100%')),), titles=('Log Output',))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "rom1 is of type PHLTIModel.\n" ] } ], "source": [ "from pymor.reductors.ph.ph_irka import PHIRKAReductor\n", "\n", "reductor = PHIRKAReductor(fom)\n", "rom1 = reductor.reduce(10)\n", "print(f'rom1 is of type {type(rom1).__qualname__}.')" ] }, { "cell_type": "markdown", "id": "55fd6800", "metadata": {}, "source": [ "### Positive-real balanced truncation (PRBT)\n", "\n", "Positive-real balanced truncation (PRBT) works analogously to the standard balanced truncation\n", "method described in {doc}`tutorial_bt`, but uses positive real controllability\n", "and observability Gramians instead. PRBT preserves passivity but returns a ROM\n", "of type `LTIModel`. Thus, we convert the ROM into a `PHLTIModel` in a\n", "post-processing step. Note that PRBT can be used with any passive `LTIModel` FOM." ] }, { "cell_type": "code", "execution_count": 4, "id": "21bb5791", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a5ea5aaa099d4452ab984798528c3b06", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', width='100%')),), titles=('Log Output',))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "rom2 is of type PHLTIModel.\n" ] } ], "source": [ "from pymor.models.iosys import PHLTIModel\n", "from pymor.reductors.bt import PRBTReductor\n", "\n", "reductor = PRBTReductor(fom)\n", "rom2 = reductor.reduce(10)\n", "rom2 = rom2.with_(solver_options={'ricc_pos_lrcf': 'slycot'})\n", "rom2 = PHLTIModel.from_passive_LTIModel(rom2)\n", "print(f'rom2 is of type {type(rom2).__qualname__}.')" ] }, { "cell_type": "markdown", "id": "a666108b", "metadata": {}, "source": [ "### Passivity preserving model reduction via spectral factorization\n", "\n", "The `SpectralFactorReductor` method\n", "is a wrapper reductor for another generic reductor. The method extracts a\n", "spectral factor from the FOM (this is only possible if the system is passive),\n", "which subsequentely is reduced by a reductor specified by the user.\n", "A spectral factor is a standard `LTIModel`, and hence any LTI reduction can be used.\n", "For our example, we use the `IRKAReductor` as the inner reductor.\n", "If the inner reductor returns a stable ROM, passivity is preserved.\n", "The spectral factor method and PRBT are related since the computation of\n", "the optimal spectral factor for model reduction depends on the computation of\n", "the positive-real observability Gramian. The spectral factor method can be used with\n", "any passive `LTIModel` FOM. Again, we convert the ROM of type `LTIModel` into a\n", "`PHLTIModel` in a post-processing step." ] }, { "cell_type": "code", "execution_count": 5, "id": "30f9663a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9947ec557586486ea6fc9a91b5f09a53", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', width='100%')),), titles=('Log Output',))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "rom3 is of type PHLTIModel.\n" ] } ], "source": [ "from pymor.reductors.spectral_factor import SpectralFactorReductor\n", "from pymor.reductors.h2 import IRKAReductor\n", "\n", "reductor = SpectralFactorReductor(fom)\n", "rom3 = reductor.reduce(\n", " lambda spectral_factor, mu : IRKAReductor(spectral_factor, mu).reduce(10)\n", ")\n", "rom3 = rom3.with_(solver_options={'ricc_pos_lrcf': 'slycot'})\n", "rom3 = PHLTIModel.from_passive_LTIModel(rom3)\n", "print(f'rom3 is of type {type(rom3).__qualname__}.')" ] }, { "cell_type": "markdown", "id": "fd31179a", "metadata": {}, "source": [ "### Comparison\n", "\n", "Let us compare the {math}`\\mathcal{H}_2` errors of the three methods:" ] }, { "cell_type": "code", "execution_count": 6, "id": "6fe870dd", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a6da687cbf004a5bb84dd4925f9b06da", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', width='100%')),), titles=('Log Output',))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "pHIRKA - Relative H2 error: 2.430e-01\n", "PRBT - Relative H2 error: 1.031e-02\n", "spectral_factor - Relative H2 error: 3.239e-03\n" ] } ], "source": [ "err1 = fom - rom1\n", "err2 = fom - rom2\n", "err3 = fom - rom3\n", "\n", "print(f'pHIRKA - Relative H2 error: {err1.h2_norm() / fom.h2_norm():.3e}')\n", "print(f'PRBT - Relative H2 error: {err2.h2_norm() / fom.h2_norm():.3e}')\n", "print(f'spectral_factor - Relative H2 error: {err3.h2_norm() / fom.h2_norm():.3e}')" ] }, { "cell_type": "markdown", "id": "2bc3e934", "metadata": {}, "source": [ "We can plot a magnitude plot of the three error systems:" ] }, { "cell_type": "code", "execution_count": 7, "id": "496d3a51", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "w = (1e-4, 1e3)\n", "fig, ax = plt.subplots()\n", "err1.transfer_function.mag_plot(w, ax=ax, label='pHIRKA')\n", "err2.transfer_function.mag_plot(w, ax=ax, linestyle='--', label='PRBT')\n", "err3.transfer_function.mag_plot(w, ax=ax, label='spectral_factor')\n", "_ = ax.legend()" ] }, { "cell_type": "markdown", "id": "44915bab", "metadata": {}, "source": [ "Download the code:\n", "{download}`tutorial_ph.md`,\n", "{nb-download}`tutorial_ph.ipynb`." ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.16.2" } }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", 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00:01 PHIRKAReductor: Generating initial interpolation data
00:01 PHIRKAReductor: Starting pH-IRKA
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 1: 1.808009e+01
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 2: 2.979038e-01
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 3: 2.343049e-01
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 4: 2.217711e-01
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 5: 1.545250e-01
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 6: 9.451058e-02
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 7: 5.975621e-02
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 8: 3.987967e-02
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 9: 2.773418e-02
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 10: 1.982008e-02
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 11: 1.561338e+00
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 12: 1.059532e-02
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 13: 7.847201e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 14: 5.841387e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 15: 4.364071e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 16: 3.269091e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 17: 2.453767e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 18: 1.844601e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 19: 1.388289e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 20: 1.045800e-03
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 21: 7.883501e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 22: 5.945968e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 23: 4.486477e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 24: 3.386310e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 25: 2.556549e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 26: 1.930472e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 27: 1.457926e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 28: 1.101172e-04
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 PHLTIPGReductor: Operator projection ...
00:01 PHLTIPGReductor: Building ROM ...
00:01 PHIRKAReductor: Convergence criterion in iteration 29: 8.317867e-05
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00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 LTIPGReductor: Operator projection ...
00:01 LTIPGReductor: Building ROM ...
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00:01 IRKAReductor: Generating initial interpolation data
00:01 IRKAReductor: Starting IRKA
00:01 gram_schmidt: Orthonormalizing vector 1 again
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 gram_schmidt: Orthonormalizing vector 1 again
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 LTIPGReductor: Operator projection ...
00:01 LTIPGReductor: Building ROM ...
00:01 IRKAReductor: Convergence criterion in iteration 1: 1.846313e+01
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 gram_schmidt: Orthonormalizing vector 1 again
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 LTIPGReductor: Operator projection ...
00:01 LTIPGReductor: Building ROM ...
00:01 IRKAReductor: Convergence criterion in iteration 2: 1.978979e+01
00:01 gram_schmidt: Orthonormalizing vector 1 again
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 gram_schmidt: Orthonormalizing vector 1 again
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 LTIPGReductor: Operator projection ...
00:01 LTIPGReductor: Building ROM ...
00:01 IRKAReductor: Convergence criterion in iteration 3: 9.705852e+00
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 gram_schmidt: Orthonormalizing vector 1 again
00:01 gram_schmidt: Orthonormalizing vector 2 again
00:01 gram_schmidt: Orthonormalizing vector 3 again
00:01 gram_schmidt: Orthonormalizing vector 4 again
00:01 gram_schmidt: Orthonormalizing vector 5 again
00:01 gram_schmidt: Orthonormalizing vector 6 again
00:01 gram_schmidt: Orthonormalizing vector 7 again
00:01 gram_schmidt: Orthonormalizing vector 8 again
00:01 gram_schmidt: Orthonormalizing vector 9 again
00:01 LTIPGReductor: Operator projection ...
00:01 LTIPGReductor: Building ROM ...
00:01 IRKAReductor: Convergence criterion in iteration 4: 1.039312e+01
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 5: 3.839316e+01
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 6: 4.731623e+00
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 7: 4.592636e+00
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 8: 9.427749e+00
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 9: 1.003186e+01
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 10: 4.421041e+01
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 11: 1.812174e+00
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 12: 8.871263e+00
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 13: 4.278655e+01
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 14: 8.487982e+00
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 15: 1.002972e+01
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 16: 1.953923e-01
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 17: 1.150771e-01
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 18: 3.669747e-02
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 19: 1.466247e-02
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 20: 5.334244e-03
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 21: 2.016507e-03
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 22: 7.508330e-04
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 23: 2.813034e-04
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 24: 1.051309e-04
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 gram_schmidt: Orthonormalizing vector 1 again
00:02 gram_schmidt: Orthonormalizing vector 2 again
00:02 gram_schmidt: Orthonormalizing vector 3 again
00:02 gram_schmidt: Orthonormalizing vector 4 again
00:02 gram_schmidt: Orthonormalizing vector 5 again
00:02 gram_schmidt: Orthonormalizing vector 6 again
00:02 gram_schmidt: Orthonormalizing vector 7 again
00:02 gram_schmidt: Orthonormalizing vector 8 again
00:02 gram_schmidt: Orthonormalizing vector 9 again
00:02 LTIPGReductor: Operator projection ...
00:02 LTIPGReductor: Building ROM ...
00:02 IRKAReductor: Convergence criterion in iteration 25: 3.933002e-05
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00:02 |WARNING|PHLTIModel: The D operator is not exactly zero (squared Frobenius norm is 2e-24).
00:02 |WARNING|LTIModel: The D operator is not exactly zero (squared Frobenius norm is 8.156630584998156e-56).
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