Sequential convex optimization

  • What is sequential convex programming?

    Sequential convex programming (SCP) is a local optimization method for nonconvex prob- lems that leverages convex optimization..

Sequential convex optimization solves a non-convex op- timization problem by repeatedly constructing a convex subproblem—an approximation to the problem around the current iterate x. The subproblem is used to generate a step ∆x that makes progress on the original problem.
Sequential convex optimization solves a non-convex op- timization problem by repeatedly constructing a convex subproblem—an approximation to the problem around the current iterate x. The subproblem is used to generate a step ∆x that makes progress on the original problem.

Are sequential convex programming algorithms effective for maximum-terminal-velocity and minimum-heat-load entry problems?

To demonstrate the effectiveness and performance of the newly proposed algorithms, numerical simulations are presented for maximum-terminal-velocity and minimum-heat-load entry problems

Results show that these two problems are not well solved using the basic sequential convex programming algorithm

Can convex programming solve nonconvex optimization problems?

We adopt the difference of convex programming technique to relax a wide variety of nonconvex optimization problems into convex programs

We extend this approach to a sequential convex programming algorithm that can generate a convergent sequence of feasible points whose objective values monotonically improve

What is sequential convex Programming (SCP)?

Sequential convex programming (SCP) is a local optimization method for nonconvex prob- lems that leverages convex optimization

The basic idea is simple: we handle the convex portions of the problem exactly and efficiently, while for the nonconvex portions of the problem, we model them by convex functions that are (at least locally) accurate


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