• # GQTPAR

• Referenced in 308 articles [sw07451]
• algorithm for the problem of minimizing a quadratic function subject to an ellipsoidal constraint ... second order necessary conditions for a minimizer of the objective function. Numerical results for GQTPAR...
• # Optimization Toolbox

• Referenced in 280 articles [sw10828]
• quadratic, integer, and nonlinear optimization problems. Optimization Toolbox™ provides functions for finding parameters that minimize ... linear programming, mixed-integer linear programming, quadratic programming, nonlinear optimization, and nonlinear least squares...
• # UOBYQA

• Referenced in 59 articles [sw07576]
• 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...
• # CVX

• Referenced in 669 articles [sw04594]
• standard problem types, including linear and quadratic programs (LPs/QPs), second-order cone programs (SOCPs ... conveniently formulate and solve constrained norm minimization, entropy maximization, determinant maximization, and many other convex...
• # TRICE

• Referenced in 46 articles [sw05197]
• sequential quadratic programming (SQP) algorithms for the solution of a class of minimization problems with ... reasonable, but more stringent, conditions on the quadratic model and on the trial steps ... nondegenerate strict local minimizer is $q$-quadratic. The results given here include, as special cases...
• # QPOPT

• Referenced in 17 articles [sw07859]
• Fortran 77 subroutines for minimizing a general quadratic function subject to linear constraints and simple ... linear equalities and inequalities. If the quadratic function is convex (i.e., the Hessian is positive ... obtained 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...
• # Algorithm 829

• Referenced in 37 articles [sw04467]
• functions are generated by defining a convex quadratic function systematically distorted by polynomials in order ... attraction region of the global minimizer ... distance from the global minimizer to the vertex of the quadratic function. Then, all other...
• # NPSOL

• Referenced in 147 articles [sw07420]
• Fortran 77 subroutines for minimizing a smooth function subject to constraints, which may include simple ... problem size. NPSOL uses a sequential quadratic programming (SQP) algorithm, in which each search direction...
• # ve08

• Referenced in 142 articles [sw05141]
• clustered eigenvalues at a minimizer x *, in which case conjugate gradient and limited memory variable ... traditional approach of approximating f by local quadratic models, which is computationally feasible even...
• # Q-MAT

• Referenced in 4 articles [sw31046]
• Computing Medial Axis Transform By Quadratic Error Minimization. The medial axis transform ... method, called Q-MAT, that uses quadratic error minimization to compute a structurally simple, geometrically...
• # SpeeDP

• Referenced in 4 articles [sw07003]
• programming (LRSDP) relaxations of unconstrained ${-1,1}$ quadratic problems (or, equivalently, of max-cut problems ... convex nonlinear programming problem of minimizing a quadratic function subject to separable quadratic equality constraints...
• # QSPLINE

• Referenced in 4 articles [sw07307]
• reformulated as an unconstrained minimization problem with a convex quadratic spline (i.e., a differentiable convex ... solving the original quadratic programming problem, in which various unconstrained minimization algorithms can be used ... finding a stationary point of the convex quadratic spline. The QSPLINE method can also ... dynamic balance between the need for minimizing the original objective function and that of forcing...
• # BOBYQA

• Referenced in 60 articles [sw04769]
• name BOBYQA denotes Bound Optimization BY Quadratic Approximation. Please send an e-mail ... most powerful package available at present for minimizing functions of hundreds of variables without derivatives...
• # QPsimplex

• Referenced in 2 articles [sw31751]
• Simplex QP-based methods for minimizing a conic quadratic objective over polyhedra. We consider minimizing ... quadratic objective over a polyhedron. Such problems arise in parametric value-at-risk minimization, portfolio ... polynomial interior point algorithms for conic quadratic optimization. However, interior point algorithms are not well...