In this work, we consider the low-rank decomposition (SDPR) of general convex semidefinite
programming (SDP) problems that contain both a positive semidefinite matrix and a …

Sequential optimality conditions have played a major role in unifying and extending global
convergence results for several classes of algorithms for general nonlinear optimization. In …

This work presents a complete workflow for the parameter identification of an MBS model
representing a vibratory conveying system. First, the MBS model built within the multibody …

This work approaches the fields of homogenization and of materials design for the linear
and nonlinear mechanical properties with prescribed properties-profile. The set of …

In this paper, we study augmented Lagrangian functions for nonlinear semidefinite
programming (NSDP) problems with exactness properties. The term exact is used in the …

A key problem in mathematical imaging, signal processing and computational statistics is
the minimization of non-convex objective functions that may be non-differentiable at the …

In constrained nonlinear optimization, the squared slack variables can be used to transform
a problem with inequality constraints into a problem containing only equality constraints …

The well known constant rank constraint qualification [Math. Program. Study 21: 110–126,
1984] introduced by Janin for nonlinear programming has been recently extended to a conic …

In this work, we are interested in nonlinear symmetric cone problems (NSCPs), which
contain as special cases nonlinear semidefinite programming, nonlinear second-order cone …

Semidefinite programming (SDP) problems have been investigated and solved in this work.
A novel approach has been introduced and validated to solve SDP relaxations of binary …