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  "body": "\n## Summary\n\nThe L-curve is a log-log plot of the size of the regularized solution against the size of the residual.\nEach point corresponds to one value of the regularization parameter.\nFor many discrete ill-posed problems the curve is L-shaped.\nThe parameter at the corner of the L balances fitting the data against amplifying the noise, and choosing it is the L-curve criterion for [[regularization|WTY54BFC46]].\n\n## Explanations\n\n### L1 human\n\nIn Tikhonov regularization a parameter controls how much the solution is smoothed.\nSmall values fit the data closely but let noise blow up the solution.\nLarge values keep the solution small but fit the data poorly.\nPlotting the two errors against each other on logarithmic axes gives an L-shaped curve with a steep part and a flat part.\nThe corner is a natural compromise, where neither error can be reduced much without a large increase of the other.\n",
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