English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a new method for linear second-order cone programs. It is based onthe sequential quadratic programming framework for nonlinear programming. Incontrast to interior point methods, it can capitalize on the warm-startcapabilities of active-set quadratic programming subproblem solvers and achievea local quadratic rate of convergence.  In order to overcome the non-differentiability or singularity observed innonlinear formulations of the conic constraints, the subproblems approximatethe cones with polyhedral outer approximations that are refined throughout theiterations. For nondegenerate instances, the algorithm implicitly identifiesthe set of cones for which the optimal solution lies at the extreme points. Asa consequence, the final steps are identical to regular sequential quadraticprogramming steps for a differentiable nonlinear optimization problem, yieldinglocal quadratic convergence.  We prove the global and local convergence guarantees of the method andpresent numerical experiments that confirm that the method can take advantageof good starting points and can achieve higher accuracy compared to astate-of-the-art interior point solver.

A Quadratically Convergent Sequential Programming Method for Second-Order Cone Programs Capable of Warm Starts