A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization
ACM Transactions on Mathematical Software (TOMS)
First-order sequential convex programming using approximate diagonal QP subproblems
Structural and Multidisciplinary Optimization
Positive definite separable quadratic programs for non-convex problems
Structural and Multidisciplinary Optimization
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We study the replacement of dual subproblems based on separable quadratic objective and separable quadratic constraint functions by classical separable quadratic programs, in which the constraints are linearized. The quadratic subprograms are then solved in the dual space, which allows for a direct assessment of the computational implications that results from linearization of the separable quadratic constraints in the first place. The solution of the linearized QP forms in the dual space seems far easier than the solution of their quadratic-quadratic counterparts, which may have important implications for algorithms aimed at very large scale optimal design.