@article{KarimiTun{\c{c}}el2023,
  author    = {Karimi, Mehdi and Tun{\c{c}}el, Levent},
  title     = {Domain-Driven Solver (DDS) Version 2.1: a MATLAB-based software package for convex optimization problems in domain-driven form},
  journal   = {Mathematical Programming Computation},
  volume    = {16},
  number    = {1},
  publisher = {Springer Science and Business Media LLC},
  issn      = {1867-2949},
  doi       = {10.1007/s12532-023-00248-2},
  pages     = {37 -- 92},
  year      = {2023},
  abstract  = {Domain-Driven Solver (DDS) is a MATLAB-based software package for convex opti- mization. The current version of DDS accepts every combination of the following function/set constraints: (1) symmetric cones (LP, SOCP, and SDP); (2) quadratic constraints that are SOCP representable; (3) direct sums of an arbitrary collection of 2-dimensional convex sets defined as the epigraphs of univariate convex functions (including as special cases geometric programming and entropy programming); (4) generalized Koecher (power) cone; (5) epigraphs of matrix norms (including as a special case minimization of nuclear norm over a linear subspace); (6) vector rela- tive entropy; (7) epigraphs of quantum entropy and quantum relative entropy; and (8) constraints involving hyperbolic polynomials. The infeasible-start primal-dual algo- rithms used for DDS rely heavily on duality theory and properties of Legendre-Fenchel conjugate functions, and are designed to rigorously determine the status of a given problem. We discuss some important implementation details and techniques we used to improve the robustness and efficiency of the software. The appendix contains many examples.},
  language  = {en}
}