CCA_ Computational convex analysis library. The Computational Convex Analysis numerical library contains efficient algorithms to manipulate convex and nonconvex univariate functions, and to compute fundamental convex analysis transforms. Functions can be entered explicitly or through a black box, and are approximated with quadratic splines. The primary transforms implemented are the Legendre-Fenchel transform, the Moreau envelope, the (generalized) proximal average, the convex hull, the subdifferential, and Rockafellar and Fitzpatrick functions. The library contains extensive demos, and numerous tests (476 unit tests with a statement coverage of 85%).
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Kumar, Deepak; Lucet, Yves: Computation of the epsilon-subdifferential of convex piecewise linear-quadratic functions in optimal worst-case time (2019)
- Haque, Tasnuva; Lucet, Yves: A linear-time algorithm to compute the conjugate of convex piecewise linear-quadratic bivariate functions (2018)
- Bajaj, Anuj; Hare, Warren; Lucet, Yves: Visualization of the (\varepsilon)-subdifferential of piecewise linear-quadratic functions (2017)
- Gardiner, Bryan; Jakee, Khan; Lucet, Yves: Computing the partial conjugate of convex piecewise linear-quadratic bivariate functions (2014)
- Gardiner, Bryan; Lucet, Yves: Computing the conjugate of convex piecewise linear-quadratic bivariate functions (2013)