This paper solves a class of robust control problems, with output feedback, for systems with mixed parametric uncertainty and unmodeled dynamics. This class of problems is characterized by a special rank-one assumption on the transfer matrix of the nominal plant. Under this assumption, the robust stabilization problem is reduced to a convex feasibility problem involving linear matrix inequaliti...

The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a...

This paper is concerned with the robust stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. By utilizing a Lyapunov-Krasovskii functional and conducting stochastic analysis, the authors show that the addressed neural networks are globally, robustly, and asymptotically stable if a convex optimization problem is feasible. Some stability criteria ...

The increase in complexity in process control goes along with an increasing need for complete and guaranteed fault diagnosis. In this contribution, we propose a set-based method for complete fault diagnosis for polynomial systems. It is based on a reformulation of the diagnosis problem as a nonlinear feasibility problem, which is subsequently relaxed into a semidefinite program. This is done by...

In many industrial and non-industrial applications, it is necessary to identify the largest inscribed rectangle in a certain shape. The problem is studied for convex and non-convex polygons. Another criterion is the direction of the rectangle: axis aligned or general. In this paper a heuristic algorithm is presented for finding the largest axis aligned inscribed rectangle in a general polygon. ...

In this paper we consider a fractional optimization problem that minimizes the ratio of two quadratic functions subject to a strictly convex quadratic constraint. First using the extension of Charnes-Cooper transformation, an equivalent homogenized quadratic reformulation of the problem is given. Then we show that under certain assumptions, it can be solved to global optimality using semidefini...

the analysis of flow in water-distribution networks with several pumps by the content model may be turned into a non-convex optimization uncertain problem with multiple solutions. newton-based methods such as gga are not able to capture a global optimum in these situations. on the other hand, evolutionary methods designed to use the population of individuals may find a global solution even for ...

In this paper we develop random block coordinate descent methods for minimizing large-scale linearly constrained convex problems over networks. Since coupled constraints appear in the problem, we devise an algorithm that updates in parallel at each iteration at least two random components of the solution, chosen according to a given probability distribution. Those computations can be performed ...

This study aims at investigation of a generalized variational inequality problem. We initiate new iterative algorithm and examine its convergence analysis. Using this newly proposed method, we estimate the common solution problem fixed points nonexpansive mapping. A numerical example is illustrated to verify our existence result. Further, demonstrate that considered converges with faster rate t...

We propose an algorithm for approximately solving the mixed packing and covering problem; given a convex compact set ∅ = B ⊆ RN , either compute x ∈ B such that f (x) ≤ (1 + )a and g(x) ≥ (1 − )b or decide that {x ∈ B | f (x) ≤ a, g(x) ≥ b} = ∅. Here f, g : B → RM + are vectors whose components are M non-negative convex and concave functions, respectively, and a, b ∈ RM ++ are constant positive...