20 citations · 20 across the 4 of their papers we have counts for
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math.OC2019
Error bound conditions and convergence of optimization methods on smooth and proximally smooth manifolds
Maxim Balashov, Andrey Tremba
We analyse the convergence of the gradient projection algorithm, which is finalized with the Newton method, to a stationary point for the problem of nonconvex constrained optimizat…
math.OC2019★ 20 cited
Sparse solutions of optimal control via Newton method for under-determined systems
Boris Polyak, Andrey Tremba
We focus on finding sparse and least--norm solutions for unconstrained nonlinear optimal control problems. Such optimization problems are non-convex and non-smooth, neverth…
math.OC2019
Gradient projection and conditional gradient methods for constrained nonconvex minimization
Maxim Balashov, Boris Polyak, Andrey Tremba
Minimization of a smooth function on a sphere or, more generally, on a smooth manifold, is the simplest non-convex optimization problem. It has a lot of applications. Our goal is t…