• PETSc

  • Referenced in 1594 articles [sw04012]
  • PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that ... Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within ... problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc...
  • SNOPT

  • Referenced in 556 articles [sw02300]
  • algorithm for large-scale constrained optimization. Sequential quadratic programming (SQP) methods have proved highly effective ... consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives...
  • NPSOL

  • Referenced in 147 articles [sw07420]
  • NPSOL 5.0: Fortran package for nonlinear programming. NPSOL is a set of Fortran 77 subroutines ... include simple bounds on the variables, linear constraints, and smooth nonlinear constraints. The user provides ... problem size. NPSOL uses a sequential quadratic programming (SQP) algorithm, in which each search direction ... solution of a QP subproblem. Bounds, linear constraints, and nonlinear constraints are treated separately. Hence...
  • filterSQP

  • Referenced in 59 articles [sw04725]
  • problems. The package implements a Sequential Quadratic Programming solver with a “filter” to promote global ... sparse linear algebra package and a robust QP solver. Keywords: Nonlinear Programming, Sequential Quadratic Programming...
  • CONOPT

  • Referenced in 171 articles [sw02791]
  • algorithm for solving large-scale nonlinear programs involving sparse nonlinear constraints. The paper will discuss ... with the popular methods based on sequential linearized subproblems forms the basis for discussions ... with other codes for large-scale nonlinear programming from both an efficiency and a reliability...
  • PNEW

  • Referenced in 77 articles [sw06157]
  • linear constraints. Subroutine PMIN, intended for minimax optimization, is based on a sequential quadratic programming...
  • SLQP-GS

  • Referenced in 11 articles [sw05162]
  • Sequential Linear or Quadratic Programming with Gradient Sampling (Matlab...
  • SQPlab

  • Referenced in 181 articles [sw05161]
  • optimal control structure. SQP stands for Sequential Quadratic Programming, a method invented ... program (QP). This is a simpler optimization problem, which has a quadratic objective and linear...
  • MISER3

  • Referenced in 84 articles [sw04190]
  • approximated by piecewise constant or piecewise linear (continuous) functions defined on suitable partitions ... into a nonlinear programming problem which is solved using a sequential quadratic programming algorithm...
  • CFSQP

  • Referenced in 61 articles [sw04658]
  • infeasible for some inequality constraint or some linear equality constraint, CFSQP first generates a feasible ... with many sequentially related constraints (or objectives), such as discretized semi- infinite programming (SIP) problems ... iteration after feasibility for nonlinear inequality and linear constraints has been reached (monotone line search ... implementation of two algorithms based on Sequential Quadratic Programming (SQP), modified so as to generate...
  • NLPQLP

  • Referenced in 40 articles [sw04073]
  • special implementation of a sequential quadratic programming (SQP) method. Proceeding from a quadratic approximation ... Lagrangian function and a linearization of constraints, a quadratic programming subproblem is formulated and solved...
  • RAxML

  • Referenced in 45 articles [sw07716]
  • data and are implemented in a sequential program called RAxML. We have demonstrated that RAxML ... outperforms the currently fastest statistical phylogeny programs (MrBayes, PHYML) in terms of speed and likelihood ... algorithm which in some cases yields super-linear speedups for an analysis of 1.000 organisms ... Bacteria and Archaea. Finally, we compare the sequential speed and accuracy of RAxML and PHYML...
  • pySLEQP

  • Referenced in 2 articles [sw17724]
  • pySLEQP A Sequential Linear Quadratic Programming Method Implemented in Python. We present a prototype implementation ... Sequential Linear Equality-Constrained Qudratic Programming (SLEQP) method for solving the nonlinear programming problem. Similar...
  • NDA

  • Referenced in 14 articles [sw04663]
  • linear constraints. Subroutine PMIN, intended for minimax optimization, is based on a sequential quadratic programming...
  • CML

  • Referenced in 10 articles [sw26227]
  • parametric constraints (linear or nonlinear, equality or inequality), using the sequential quadratic programming method...
  • OOPS

  • Referenced in 33 articles [sw10470]
  • Parallel interior point solver for structured linear programs. Issues of implementation of an object-oriented ... arising in the optimization of networks. The sequential implementation outperforms the state...
  • FLAME

  • Referenced in 39 articles [sw00293]
  • programming and maintaining families of algorithms for a broad spectrum of linear algebra operations ... greatly simplified. In combination with our formal linear algebra methods environment (FLAME) approach to deriving ... algorithms, dozens of algorithms for a single linear algebra operation can be derived, verified ... combination with an extension of the parallel linear algebra package (PLAPACK) API, the approach presents...
  • TRICE

  • Referenced in 46 articles [sw05197]
  • family of trust-region interior-point sequential quadratic programming (SQP) algorithms for the solution ... variables is described and analyzed. Such nonlinear programs arise, e.g., from the discretization of optimal ... rely on matrix factorizations of the linearized constraints but use solutions of the linearized state...
  • e04ucf

  • Referenced in 5 articles [sw30978]
  • variables, linear constraints and smooth nonlinear constraints) using a sequential quadratic programming (SQP) method...
  • WORHP

  • Referenced in 20 articles [sw10824]
  • core foundations as a sparse sequential quadratic programming (SQP) / interior-point (IP) method; it includes ... update techniques for Hessian approximations, and sparse linear algebra. Furthermore it is based on reverse...