• # CPLEX

• Referenced in 2625 articles [sw04082]
• related problems. Specifically, it solves linearly or quadratically constrained optimization problems where the objective ... linear function or a convex quadratic function. The variables in the model may be declared...
• # Algorithm 829

• Referenced in 56 articles [sw04467]
• generated by defining a convex quadratic function systematically distorted by polynomials in order to introduce ... minimizer to the vertex of the quadratic function. Then, all other necessary parameters are generated...
• # Optimization Toolbox

• Referenced in 288 articles [sw10828]
• Solve linear, quadratic, integer, and nonlinear optimization problems. Optimization Toolbox™ provides functions for finding parameters ... linear programming, mixed-integer linear programming, quadratic programming, nonlinear optimization, and nonlinear least squares...
• # FPC_AS

• Referenced in 63 articles [sw12218]
• norm ∥x∥ 1 to a linear function of x. The resulting subspace problem, which involves ... minimization of a smaller and smooth quadratic function, is solved in the second phase...
• # SNOPT

• Referenced in 524 articles [sw02300]
• algorithm for large-scale constrained optimization. Sequential quadratic programming (SQP) methods have proved highly effective ... solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here...
• # CVX

• Referenced in 745 articles [sw04594]
• standard problem types, including linear and quadratic programs (LPs/QPs), second-order cone programs (SOCPs ... convex optimization problems, including many involving nondifferentiable functions, such as ℓ1 norms...
• # UOBYQA

• Referenced in 64 articles [sw07576]
• UOBYQA: unconstrained optimization by quadratic approximation. A new algorithm for general unconstrained optimization calculations ... curvature of the objective function by forming quadratic models by interpolation. Obviously, no first derivatives ... vector of variables either by minimizing the quadratic model subject to a trust region bound ... error of the quadratic approximation of the function being minimized. It is pointed out that...
• # YALMIP

• Referenced in 963 articles [sw04595]
• used for linear programming, quadratic programming, second order cone programming, semidefinite programming, non-convex semidefinite ... define your constraints and objective functions using intuitive and standard MATLAB code. Automatic categorization...
• # QPOPT

• Referenced in 17 articles [sw07859]
• subroutines for minimizing a general quadratic function subject to linear constraints and simple upper ... linear equalities and inequalities. If the quadratic function is convex (i.e., the Hessian is positive ... will be a global minimizer. If the quadratic is non-convex (i.e., the Hessian ... infeasibilities. The second phase minimizes the quadratic function within the feasible region, using a reduced...
• # NLPQLP

• Referenced in 40 articles [sw04073]
• method. Proceeding from a quadratic approximation of the Lagrangian function and a linearization of constraints ... quadratic programming subproblem is formulated and solved by QL. Depending on the number of nodes ... distributed system, objective and constraint functions can be evaluated simultaneously at predetermined test points along...
• # SQOPT

• Referenced in 18 articles [sw07860]
• software package for minimizing a convex quadratic function subject to both equality and inequality constraints ... some of the variables appear in the quadratic term, or the number of active constraints ... constraint matrices. A quadratic term 1/2x’Hx in the objective function is represented...
• # NPSOL

• Referenced in 147 articles [sw07420]
• subroutines to define the objective and constraints functions and (optionally) their first derivatives. NPSOL ... problem size. NPSOL uses a sequential quadratic programming (SQP) algorithm, in which each search direction ... especially effective if the objective or constraint functions are expensive to evaluate...