A relaxed version of Bregman's method for convex programming
Journal of Optimization Theory and Applications
A fast computational algorithm for the Legendre-Fenchel transform
Computational Optimization and Applications
Nonlinear rescaling and proximal-like methods in convex optimization
Mathematical Programming: Series A and B
A normal form for function rings of piecewise functions
Journal of Symbolic Computation
A Logarithmic-Quadratic Proximal Method for Variational Inequalities
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Convex Optimization
A canonical form for piecewise defined functions
Proceedings of the 2007 international symposium on Symbolic and algebraic computation
The piecewise linear-quadratic model for computational convex analysis
Computational Optimization and Applications
Hi-index | 0.00 |
Convex optimization deals with certain classes of mathematical optimization problems including least-squares and linear programming problems. This area has recently been the focus of considerable study and interest due to the facts that convex optimization problems can be solved efficiently by interior-point methods and that convex optimization problems are actually much more prevalent in practice that previously thought.Key notions in convex optimization are the Fenchel conjugate and the subdifferential of a convex function. In this paper, we build a new bridge between convex optimization and symbolic mathematics by describing the Maple package fenchel, which allows for the symbolic computation of these objects for numerous convex functions defined on the real line. We are able to symbolically reproduce computations for finding Fenchel conjugates and subdifferentials for numerous nontrivial examples found in the literature.