Quadratic optimization lies at the very heart of many structural pattern recognition and
computer vision problems, such as graph matching, object recognition, image segmentation …

We propose a novel smoothing active set method for linearly constrained non-Lipschitz
nonconvex problems. At each step of the proposed method, we approximate the objective …

To solve general convex quadratic programming problems with bound constraints, this
paper proposes a new interior point iterative method that is easy to be implemented. 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 …

Privacy preserving mechanisms such as differential privacy inject additional randomness in
the form of noise in the data, beyond the sampling mechanism. Ignoring this additional noise …

In this paper, we propose an interior-point method for linearly constrained---and possibly
nonconvex---optimization problems. The method---which we call the Hessian barrier …

Interior point methods for solving linearly constrained convex programming involve a
variable projection matrix at each iteration to deal with the linear constraints. This matrix …

A so-called Standard Bi-Quadratic Optimization Problem (StBQP) consists in minimizing a bi-
quadratic form over the Cartesian product of two simplices (so this is different from a Bi …

Recently an affine scaling, interior point algorithm ASL was developed for box constrained
optimization problems with a single linear constraint (Gonzalez-Lima et al., SIAM J. Optim …

Global optimization is a highly active research field in the intersection of continuous and
combinatorial optimization (a basic web search delivers overa million hits for this phrase and …