• # CVX

• Referenced in 832 articles [sw04594]
• standard problem types, including linear and quadratic programs (LPs/QPs), second-order cone programs (SOCPs ... also solve much more complex convex optimization problems, including many involving nondifferentiable functions, such ... conveniently formulate and solve constrained norm minimization, entropy maximization, determinant maximization, and many other convex...
• # QPOPT

• Referenced in 17 articles [sw07859]
• convex quadratic programming. QPOPT is a set of Fortran 77 subroutines for minimizing a general ... equalities and inequalities. If the quadratic function is convex (i.e., the Hessian is positive definite ... will be a global minimizer. If the quadratic is non-convex (i.e., the Hessian ... first phase minimizes the sum of infeasibilities. The second phase minimizes the quadratic function within...
• # QSPLINE

• Referenced in 4 articles [sw07307]
• reformulated as an unconstrained minimization problem with a convex quadratic spline (i.e., a differentiable convex ... minimization algorithms can be used to find a stationary point of the convex quadratic spline ... finding a stationary point of the convex quadratic spline. The QSPLINE method can also ... strategy for robust reduction of the convex quadratic spline when line search in a Newton...
• # Algorithm 829

• Referenced in 58 articles [sw04467]
• functions are generated by defining a convex quadratic function systematically distorted by polynomials in order ... attraction region of the global minimizer, (v) distance from the global minimizer to the vertex...
• # 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...
• # QPBOX

• Referenced in 6 articles [sw04799]
• quadratic functions We consider the strictly convex quadratic programming problem with bounded variables. A dual ... Lagrange duality. The dual problem is the minimization of an unconstrained, piecewise quadratic function...
• # SpaRSA

• Referenced in 3 articles [sw20467]
• minimize an objective function that includes a quadratic error term added to a sparsity-inducing ... more general problem of minimizing the sum of a smooth convex function and a nonsmooth ... solving an optimization subproblem involving a quadratic term with diagonal Hessian (i.e., separable...
• # L2CXFT

• Referenced in 9 articles [sw00498]
• measurements of a convex function contaminated by random errors. The method minimizes ... starting point of a dual-feasible quadratic programming algorithm that completes the calculation...
• # LPCCbnc

• Referenced in 5 articles [sw31750]
• with linear complementarity constraints (LPCC) requires the minimization of a linear objective over ... problems, including bilevel programs, Stackelberg games, inverse quadratic programs, and problems involving equilibrium constraints ... instances generated from bilevel programs with convex quadratic lower level problems...
• # QPsimplex

• Referenced in 5 articles [sw31751]
• problems arise in parametric value-at-risk minimization, portfolio optimization, and robust optimization with ellipsoidal ... polynomial interior point algorithms for conic quadratic optimization. However, interior point algorithms are not well ... starts necessary for the efficient solution of convex relaxations repeatedly at the nodes...
• # ralgb4

• Referenced in 4 articles [sw22652]
• methods ralgb5 and ralgb4 for minimization of ravine-like convex functions. We consider properties ... experiments for an essentially ravine-like piecewise quadratic function and a piecewise linear function related...
• # ralgb5

• Referenced in 4 articles [sw22653]
• methods ralgb5 and ralgb4 for minimization of ravine-like convex functions. We consider properties ... experiments for an essentially ravine-like piecewise quadratic function and a piecewise linear function related...
• # 2-Phase NSGA II

• Referenced in 3 articles [sw28524]
• calculated by expected return and to minimize the risk. Variance has been considered ... convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could...
• # CXFTV2

• Referenced in 3 articles [sw00180]
• second divided differences (convexity). The method employs a dual active set quadratic programming technique that ... each active set calculation to an unconstrained minimization with fewer variables that requires only...
• # QPDO

• Referenced in 1 article [sw41548]
• QPDO, a primal-dual method for convex quadratic programs which builds upon and weaves together ... interpret the proximal operator as the unconstrained minimization of the primal-dual proximal augmented Lagrangian ... exploit warm starting, while requiring only convexity. We present details of our open-source ... robust, and efficient numerical method for convex quadratic programming...
• # DDS

• Referenced in 2 articles [sw36808]
• symmetric cones (LP, SOCP, and SDP); (2) quadratic constraints that are SOCP representable; (3) direct ... dimensional convex sets defined as the epigraphs of univariate convex functions (including as special cases ... matrix norms (including as a special case minimization of nuclear norm over a linear subspace...