• iPiasco

  • Referenced in 22 articles [sw13492]
  • iPiasco: inertial proximal algorithm for strongly convex optimization. In this paper, we present a forward ... inertial term for solving a strongly convex optimization problem of a certain type. The strongly ... smooth convex and a smooth convex function. This additional knowledge is used for deriving ... provably optimal worst-case rate of convergence for smooth strongly convex functions. We demonstrate...
  • AlphaECP

  • Referenced in 44 articles [sw04940]
  • problems and global optimal solutions can be ensured for pseudo-convex MINLP problems ... plane method which was originally given for convex NLP problems (Kelley, 1960). The method requires ... problems may be solved to optimality, but can also be solved to feasibility or only ... Pörn R. (2002). Solving Pseudo-Convex Mixed Integer Optimization Problems by Cutting Plane Techniques. Optimization...
  • FilMINT

  • Referenced in 44 articles [sw06197]
  • based branch-and-bound algorithm for convex MINLP optimization problems.” Comput. Chemical Engrg ... these enhancements, an effective solver for convex MINLPs is constructed...
  • NESUN

  • Referenced in 26 articles [sw28733]
  • gradient method: Universal gradient methods for convex optimization problems. In this paper, we present ... methods for black-box convex minimization. They do not need to know in advance ... rate of convergence, typical for the smooth optimization problems, sometimes can be achieved even...
  • ECOS

  • Referenced in 25 articles [sw12123]
  • consequence, it can be used to solve optimization problems on any embedded system for which ... efficient standard algorithm for solving convex optimization problems. It uses regularization and iterative refinement techniques...
  • CONLIN

  • Referenced in 41 articles [sw14151]
  • CONLIN: An efficient dual optimizer based on convex approximation concepts. The Convex Linearization method (CONLIN ... applicable to a broad class of structural optimization problems. The method employs mixed design variables ... function and to the constraints. The primary optimization problem is therefore replaced with a sequence ... simple algebraic structure. The explicit subproblems are convex and separable, and they can be solved...
  • LS-SVMlab

  • Referenced in 23 articles [sw07367]
  • minimization. In the methods one solves convex optimization problems, typically quadratic programs. Least Squares Support...
  • PESTO

  • Referenced in 15 articles [sw20864]
  • first-order methods for composite convex optimization. We provide a framework for computing the exact ... based first-order methods for composite convex optimization, including those performing explicit, projected, proximal, conditional ... worst-case guarantees and explicit instances of optimization problems on which the algorithm reaches this ... worst-case to solving a convex semidefinite program, generalizing previous works on performance estimation...
  • SPGL1

  • Referenced in 143 articles [sw08365]
  • parameter determines a curve that traces the optimal trade-off between the least-squares ... solution. We prove that this curve is convex and continuously differentiable over all points ... gives an explicit relationship to two other optimization problems closely related to BPDN. We describe...
  • L1-MAGIC

  • Referenced in 18 articles [sw12430]
  • MATLAB routines for solving the convex optimization programs central to compressive sampling. The algorithms...
  • ARock

  • Referenced in 17 articles [sw16800]
  • abstracts many problems in numerical linear algebra, optimization, and other areas of data science ... cases of ARock for linear systems, convex optimization, and machine learning, as well as distributed...
  • Sdpsol

  • Referenced in 16 articles [sw00840]
  • YALMIP. The much newer general purpose convex optimization package CVX serves the same purpose (better...
  • alphaBB

  • Referenced in 49 articles [sw06249]
  • global optimization method, ffBB, for general continuous optimization problems involving nonconvexities in the objective function ... bilinear, fractional, signomial) with customized tight convex lower bounding functions and (ii) by utilizing ... series of nonlinear convex minimization problems. The global optimization method, ffBB, is implemented...
  • fenchel

  • Referenced in 8 articles [sw21480]
  • Symbolic computation of Fenchel conjugates. Convex optimization deals with certain classes of mathematical optimization problems ... 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...
  • GloMIQO

  • Referenced in 63 articles [sw06266]
  • Specific instantiations of MIQCQP in process networks optimization problems include: pooling problems, distillation sequences, wastewater ... formulated as MIQCQP include: point packing, cutting convex shapes from rectangles, maximizing ... area of a convex polygon, and chip layout and compaction. Portfolio optimization in financial engineering...
  • GGPLAB

  • Referenced in 14 articles [sw04344]
  • GGPs.A variety of examples.Some caveats:The convex optimization toolbox CVX now supports GP. We recommend...
  • isotone

  • Referenced in 25 articles [sw20811]
  • give a general framework for isotone optimization. First we discuss a generalized version ... violators algorithm (PAVA) to minimize a separable convex function with simple chain constraints. Besides ... general convex functions we extend existing PAVA implementations in terms of observation weights, approaches ... measurement designs. Since isotone optimization problems can be formulated as convex programming problems with linear...
  • SCALCG

  • Referenced in 89 articles [sw08453]
  • SCALCG – Scaled conjugate gradient algorithms for unconstrained optimization. In this work we present and analyze ... conditions it is shown that, for strongly convex functions, the algorithm is global convergent. Preliminary ... consisting of 500 unconstrained optimization test problems, show that this new scaled conjugate gradient algorithm...
  • PhaseMax

  • Referenced in 8 articles [sw24954]
  • formulate phase retrieval as a convex optimization problem, which we call PhaseMax. Unlike other convex...
  • TILOS

  • Referenced in 8 articles [sw11680]
  • which couples synchronous timing analysis with convex optimization techniques, is presented ... following three programs is shown to be convex: 1) Minimize A subject ... particular class of functions called posynomials. Convex programs have many pleasant properties, and chief among ... that any point found to be locally optimal is certain to be globally optimal TILOS...