Durfee, David, Gao, Yu, Rao, Anup B and Wild, Sebastian ORCID: 0000-0002-6061-9177
(2019)
Efficient Second-Order Shape-Constrained Function Fitting.
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Abstract
We give an algorithm to compute a one-dimensional shape-constrained function that best fits given data in weighted-$L_{\infty}$ norm. We give a single algorithm that works for a variety of commonly studied shape constraints including monotonicity, Lipschitz-continuity and convexity, and more generally, any shape constraint expressible by bounds on first- and/or second-order differences. Our algorithm computes an approximation with additive error $\varepsilon$ in $O\left(n \log \frac{U}{\varepsilon} \right)$ time, where $U$ captures the range of input values. We also give a simple greedy algorithm that runs in $O(n)$ time for the special case of unweighted $L_{\infty}$ convex regression. These are the first (near-)linear-time algorithms for second-order-constrained function fitting. To achieve these results, we use a novel geometric interpretation of the underlying dynamic programming problem. We further show that a generalization of the corresponding problems to directed acyclic graphs (DAGs) is as difficult as linear programming.
Item Type: | Conference or Workshop Item (Unspecified) |
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Additional Information: | accepted for WADS 2019; (v2 fixes various typos) |
Uncontrolled Keywords: | cs.DS, cs.DS, cs.LG |
Depositing User: | Symplectic Admin |
Date Deposited: | 15 Aug 2019 14:38 |
Last Modified: | 15 Mar 2024 16:14 |
DOI: | 10.1007/978-3-030-24766-9_29 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3051854 |
Available Versions of this Item
- Efficient Second-Order Shape-Constrained Function Fitting. (deposited 15 Aug 2019 14:38) [Currently Displayed]