Correcting for unknown errors in sparse high-dimensional function approximation


We consider sparsity-based techniques for the approximation of high-dimensional functions from random pointwise evaluations. To date, almost all the works published in this field contain some a priori assumptions about the error corrupting the samples that are hard to verify in practice. In this paper, we instead focus on the scenario where the error is unknown. We study the performance of four sparsity-promoting optimization problems: weighted quadratically-constrained basis pursuit, weighted LASSO, weighted square-root LASSO, and weighted LAD-LASSO. From the theoretical perspective, we prove uniform recovery guarantees for these decoders, deriving recipes for the optimal choice of the respective tuning parameters. On the numerical side, we compare them in the pure function approximation case and in applications to uncertainty quantification of ODEs and PDEs with random inputs. Our main conclusion is that the lesser-known square-root LASSO is better suited for high-dimensional approximation than the other procedures in the case of bounded noise, since it avoids (both theoretically and numerically) the need for parameter tuning.

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  1. 1.

    The factor \(1/\sqrt{m}\) is needed in order to guarantee the restricted isometry property for the design matrix \({\varvec{A}}\). See Sect. 5.

  2. 2.

    In practice, for WLAD-LASSO we use \(\lambda = 1.01\) instead of \(\lambda = 1\) since the choice \(\lambda = 1\) leads to the presence of spurious outliers in the box plot. We think that this behavior is due to CVX and not to the decoder itself.

  3. 3.

    Notice that the outliers are sometimes aligned (e.g., in the tail of the blue curve in Fig. 1 bottom right). This is due to the structure of the proposed numerical experiment: for each randomized choice of samples, all the parameters are tested using the same samples.

  4. 4.

    We have performed the same experiment for quantities of interest different from (35), such as the integral of \(u_{{\varvec{t}}}\) over the regions \(\Omega _i\), pointwise evaluations of \(u_{\varvec{t}}\), or the integral of \(u_{{\varvec{t}}}^2\) over \(\Omega _i\) or \(\Omega _F\). In all these cases, we do not observe the strict global minima in the parameter-vs-error plot. These experiment are not reported here for the sake of brevity.

  5. 5.

    The same phenomenon is observed for WLASSO and WLAD-LASSO, but the plots are not shown here for the sake of brevity.

  6. 6.

    Notice that since \({\varvec{M}}({\varvec{z}}-\hat{{\varvec{z}}}) = {\varvec{0}}\) the constant \(\tau \) does not appear in (72). Indeed, we are just using a 2-level weighted version of the so-called stable null space property (see [31, §4.2]).


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BA, AB and SB acknowledge the Natural Sciences and Engineering Research Council of Canada through Grant 611675 and the Alfred P. Sloan Foundation and the Pacific Institute for the Mathematical Sciences (PIMS) Collaborative Research Group “High-Dimensional Data Analysis”. SB acknowledges the support of the PIMS Post-doctoral Training Center in Stochastics. The authors are grateful to Claire Boyer, John Jakeman, Richard Lockhart, Akil Narayan, and Clayton G. Webster for interesting and fruitful discussions.

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Adcock, B., Bao, A. & Brugiapaglia, S. Correcting for unknown errors in sparse high-dimensional function approximation. Numer. Math. 142, 667–711 (2019).

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Mathematics Subject Classification

  • 65D15
  • 41A10
  • 94A20