272 lines
10 KiB
Python
272 lines
10 KiB
Python
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import pandas as pd
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from matplotlib import pyplot as plt, ticker
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import seaborn as sns
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from config import COMPARISON_RESULTS, PLOTS_PATH
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def nanoseconds_to_seconds_2f(x, pos):
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return f"{x / 10 ** 9:.2f}"
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def nanoseconds_to_seconds_3f(x, pos):
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return f"{x / 10 ** 9:.3f}"
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def bytes_to_gib(x, pos):
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return f"{x / 1024 ** 3:.2f}"
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def QR_vs_LBFGS_time(conditioning, dimension="n"):
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if conditioning not in ['ill', 'well']:
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raise ValueError("conditioning must be one of 'ill', 'well'")
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if dimension not in ['n', 'm']:
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raise ValueError("dimension must be one of 'n', 'm'")
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if dimension == 'n':
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df = pd.read_csv(COMPARISON_RESULTS.format("QRvsLBFGS-n-m200-" + conditioning + "cond--time.csv"))
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else:
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df = pd.read_csv(COMPARISON_RESULTS.format("QRvsLBFGS-m-n50-" + conditioning + "cond--time.csv"))
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sns.set(style="whitegrid")
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sns.set_context('paper')
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fig, ax1 = plt.subplots(figsize=(11, 8))
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sns.lineplot(data=df, x=dimension, y='meantimeQR', marker='o', label='Mean Time QR', color='blue', ax=ax1)
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ax1.fill_between(
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df[dimension],
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df['meantimeQR'] - df['stdtimeQR'],
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df['meantimeQR'] + df['stdtimeQR'],
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alpha=0.3,
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label='Standard Deviation QR',
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color='lightblue'
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)
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sns.lineplot(data=df, x=dimension, y='meantimeLBFGS', marker='o', label='Mean Time L-BFGS', color='red', ax=ax1)
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ax1.fill_between(
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df[dimension],
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df['meantimeLBFGS'] - df['stdtimeLBFGS'],
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df['meantimeLBFGS'] + df['stdtimeLBFGS'],
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alpha=0.3,
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label='Standard Deviation L-BFGS',
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color='lightsalmon'
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)
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ax1.set_title('QR vs LBFGS time and memory scalability on ' + conditioning + '-conditioned matrix', fontsize=20,
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pad=20,
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fontweight='bold')
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ax1.set_xlabel(dimension, fontsize=15, labelpad=20)
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ax1.set_ylabel('time (s)', fontsize=15, labelpad=20)
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ax1.set_ylim(0, None)
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ax1.legend(loc='upper left', fontsize=15)
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ax1.tick_params(axis='both', which='major', labelsize=15)
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ax1.yaxis.set_major_formatter(ticker.FuncFormatter(nanoseconds_to_seconds_2f))
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ax2 = ax1.twinx()
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sns.lineplot(data=df, x=dimension, y='meanallocsQR', marker='o', label='GiB Allocation QR', color='green', ax=ax2)
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sns.lineplot(data=df, x=dimension, y='meanallocsLBFGS', marker='o', label='GiB Allocation BFGS', color='purple',
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ax=ax2)
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ax2.set_ylabel('memory (GiB)', fontsize=15, labelpad=20)
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ax2.set_ylim(0, None)
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ax2.legend(loc='upper right', fontsize=15)
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ax2.tick_params(axis='y', labelsize=15)
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ax2.yaxis.set_major_formatter(ticker.FuncFormatter(bytes_to_gib))
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plt.savefig(PLOTS_PATH.format("comparison", "QRvsLBFGS-scalability-time-" + conditioning + f"cond-{dimension}.png"))
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plt.show()
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def BFGSvsLBFGS_time(dimension="n"):
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if dimension not in ['n', 'm']:
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raise ValueError("dimension must be one of 'n', 'm'")
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if dimension == 'n':
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df = pd.read_csv(COMPARISON_RESULTS.format("BFGSvsLBFGS-n-m200--time.csv"))
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else:
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df = pd.read_csv(COMPARISON_RESULTS.format("BFGSvsLBFGS-m-n50--time.csv"))
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sns.set(style="whitegrid")
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sns.set_context('paper')
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fig, ax1 = plt.subplots(figsize=(11, 8))
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sns.lineplot(data=df, x=dimension, y='meantimeBFGS', marker='o', label='Mean Time BFGS', color='blue', ax=ax1)
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ax1.fill_between(
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df[dimension],
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df['meantimeBFGS'] - df['stdtimeBFGS'],
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df['meantimeBFGS'] + df['stdtimeBFGS'],
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alpha=0.3,
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label='Standard Deviation BFGS',
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color='lightblue'
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)
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sns.lineplot(data=df, x=dimension, y='meantimeLBFGS', marker='o', label='Mean Time L-BFGS', color='red', ax=ax1)
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ax1.fill_between(
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df[dimension],
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df['meantimeLBFGS'] - df['stdtimeLBFGS'],
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df['meantimeLBFGS'] + df['stdtimeLBFGS'],
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alpha=0.3,
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label='Standard Deviation L-BFGS',
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color='lightsalmon'
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)
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ax1.set_title('BFGS vs L-BFGS time and memory scalability on well-conditioned matrix', fontsize=20, pad=20, fontweight='bold')
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ax1.set_xlabel(dimension, fontsize=15, labelpad=20)
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ax1.set_ylabel('time (s)', fontsize=15, labelpad=20)
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ax1.set_ylim(0, None)
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if dimension == 'n':
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ax1.legend(loc='upper right', fontsize=15)
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else:
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ax1.legend(loc='upper left', fontsize=15)
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ax1.tick_params(axis='both', which='major', labelsize=15)
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ax1.yaxis.set_major_formatter(ticker.FuncFormatter(nanoseconds_to_seconds_3f))
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ax2 = ax1.twinx()
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sns.lineplot(data=df, x=dimension, y='meanallocsBFGS', marker='o', label='Bytes Allocation BFGS', color='green',
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ax=ax2)
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sns.lineplot(data=df, x=dimension, y='meanallocsLBFGS', marker='o', label='Bytes Allocation L-BFGS', color='purple',
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ax=ax2)
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ax2.set_ylabel('memory (B)', fontsize=15, labelpad=20)
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ax2.set_ylim(0, None)
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ax2.legend(loc='lower right', fontsize=15)
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ax2.tick_params(axis='y', labelsize=15)
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ax2.yaxis.set_major_formatter(ticker.FuncFormatter(bytes_to_gib))
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plt.savefig(PLOTS_PATH.format("comparison", f"BFGSvsLBFGS-time-{dimension}.png"))
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plt.show()
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def quasi_newton_time(conditioning="well", dogleg=False):
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df = pd.read_csv(COMPARISON_RESULTS.format(f"Quasi-Newton-Comparison-time-{conditioning}cond.csv"))
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sns.set(style="whitegrid")
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sns.set_context('paper')
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plt.figure(figsize=(11, 8))
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sns.lineplot(data=df, x='m', y='meantimeLBFGS', marker='o', label='L-BFGS', color='blue')
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plt.fill_between(
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df['m'],
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df['meantimeLBFGS'] - df['stdtimeLBFGS'],
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df['meantimeLBFGS'] + df['stdtimeLBFGS'],
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alpha=0.3,
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label='Standard Deviation L-BFGS',
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color='lightblue'
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)
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sns.lineplot(data=df, x='m', y='meantimeBFGS', marker='o', label='BFGS', color='red')
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plt.fill_between(
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df['m'],
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df['meantimeBFGS'] - df['stdtimeBFGS'],
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df['meantimeBFGS'] + df['stdtimeBFGS'],
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alpha=0.3,
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label='Standard Deviation BFGS',
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color='lightsalmon'
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)
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sns.lineplot(data=df, x='m', y='meantimeDFP', marker='o', label='DFP', color='purple')
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plt.fill_between(
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df['m'],
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df['meantimeDFP'] - df['stdtimeDFP'],
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df['meantimeDFP'] + df['stdtimeDFP'],
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alpha=0.3,
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label='Standard Deviation DFP',
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color='violet'
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)
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sns.lineplot(data=df, x='m', y='meantimeSR1', marker='o', label='SR1', color='violet')
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plt.fill_between(
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df['m'],
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df['meantimeSR1'] - df['stdtimeSR1'],
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df['meantimeSR1'] + df['stdtimeSR1'],
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alpha=0.3,
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label='Standard Deviation SR1',
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color='lightsalmon'
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)
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if dogleg:
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sns.lineplot(data=df, x='m', y='meantimeDFPDogleg', marker='o', label='DFP with Dogleg', color='orange')
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plt.fill_between(
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df['m'],
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df['meantimeDFPDogleg'] - df['stdtimeDFPDogleg'],
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df['meantimeDFPDogleg'] + df['stdtimeDFPDogleg'],
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alpha=0.3,
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label='Standard Deviation DFP with Dogleg',
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color='lightsalmon'
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)
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sns.lineplot(data=df, x='m', y='meantimeBFGSDogleg', marker='o', label='BFGS with Dogleg', color='green')
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plt.fill_between(
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df['m'],
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df['meantimeBFGSDogleg'] - df['stdtimeBFGSDogleg'],
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df['meantimeBFGSDogleg'] + df['stdtimeBFGSDogleg'],
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alpha=0.3,
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label='Standard Deviation BFGS with Dogleg',
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color='lightgreen'
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)
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plt.title(f'Quasi-Newton methods running time on {conditioning}-conditioned matrix', fontsize=20, pad=20,
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fontweight='bold')
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plt.xlabel('m', fontsize=15, labelpad=20)
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plt.ylabel('time (s)', fontsize=15, labelpad=20)
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plt.ylim(0, None)
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handles, labels = plt.gca().get_legend_handles_labels()
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keyword = 'Standard Deviation'
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filtered_handles = [handle for label, handle in zip(labels, handles) if keyword not in label]
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filtered_labels = [label for label in labels if keyword not in label]
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plt.legend(filtered_handles, filtered_labels, fontsize=15, loc='upper left')
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plt.xticks(fontsize=15)
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plt.yticks(fontsize=15)
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plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(nanoseconds_to_seconds_2f))
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if dogleg:
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plt.savefig(PLOTS_PATH.format("comparison", f"Quasi-Newton-Comparison-time-{conditioning}cond-Dogleg.png"))
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else:
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plt.savefig(PLOTS_PATH.format("comparison", f"Quasi-Newton-Comparison-time-{conditioning}cond.png"))
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plt.show()
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def quasi_newton_memory(conditioning="well"):
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df = pd.read_csv(COMPARISON_RESULTS.format(f"Quasi-Newton-Comparison-time-{conditioning}cond.csv"))
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sns.set(style="whitegrid")
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sns.set_context('paper')
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plt.figure(figsize=(11, 8))
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sns.lineplot(data=df, x='m', y='meanallocsLBFGS', marker='o', label='L-BFGS', color='blue')
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sns.lineplot(data=df, x='m', y='meanallocsBFGS', marker='o', label='BFGS', color='red')
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sns.lineplot(data=df, x='m', y='meanallocsBFGSDogleg', marker='o', label='BFGS with Dogleg', color='green')
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sns.lineplot(data=df, x='m', y='meanallocsDFP', marker='o', label='DFP', color='purple')
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sns.lineplot(data=df, x='m', y='meanallocsDFPDogleg', marker='o', label='DFP with Dogleg', color='orange')
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sns.lineplot(data=df, x='m', y='meanallocsSR1', marker='o', label='SR1', color='violet')
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plt.title(f'Quasi-Newton methods memory allocation on {conditioning}-conditioned matrix', fontsize=20, pad=20, fontweight='bold')
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plt.xlabel('m', fontsize=15, labelpad=20)
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plt.ylabel('bytes (GiB)', fontsize=15, labelpad=20)
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plt.ylim(-1, None)
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plt.legend(loc='upper left', fontsize=15)
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plt.xticks(fontsize=15)
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plt.yticks(fontsize=15)
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plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(bytes_to_gib))
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plt.savefig(PLOTS_PATH.format("comparison", f"Quasi-Newton-Comparison-memory-{conditioning}cond.png"))
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plt.show()
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if __name__ == '__main__':
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QR_vs_LBFGS_time("well", "m")
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QR_vs_LBFGS_time("well", "n")
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QR_vs_LBFGS_time("ill", "m")
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QR_vs_LBFGS_time("ill", "n")
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BFGSvsLBFGS_time("n")
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BFGSvsLBFGS_time("m")
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quasi_newton_time("ill")
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quasi_newton_time("well", dogleg=True)
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quasi_newton_time("well", dogleg=False)
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quasi_newton_memory("ill")
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quasi_newton_memory("well")
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