This contribution presents a kinetic model identification scheme that guarantees convergence to global optimality. The use of the extent-based incremental approach allows one to (i) identify each reaction individually, and (ii) reduce the number of parameters to identify via optimization to the ones that appear nonlinearly in the investigated rate law. Via Taylor expansion, the identification p...

In this paper, we develop a numerically efficient scheme for setmembership prediction and filtering for discrete-time nonlinear systems, that takes into explicit account the effects of nonlinearities via local second-order information. The filtering scheme is based on a classical prediction/update recursion that requires at each step the solution of a convex semidefinite optimization problem. T...

This paper presents a genetic algorithm method for solving convex quadratic bilevel programming problem. Bilevel programming problems arise when one optimization problem, the upper problem, is constrained by another optimization, the lower problem. In this paper, the bilevel convex quadratic problem is transformed into a single level problem by applying Kuhn-Tucker conditions, and then an effic...

We consider the problem of approximating the global maximum of a quadratic program (QP) subject to convex non-homogeneous quadratic constraints. We prove an approximation quality bound that is related to a condition number of the convex feasible set; and it is the currently best for approximating certain problems, such as quadratic optimization over the assignment polytope, according to the bes...

This paper studies the relationship between the so-called bi-quadratic optimization problem and its semidefinite programming (SDP) relaxation. It is shown that each r -bound approximation solution of the relaxed bi-linear SDP can be used to generate in randomized polynomial time an O(r)-approximation solution of the original bi-quadratic optimization problem, where the constant in O(r) does not...

where c ∈ R, g : R → R is non-linear and smooth on its domain, and Ω is a nonempty closed convex subset in R. This paper introduces sequential convex programming (SCP), a local optimization method for solving the nonconvex problem (P). We prove that under acceptable assumptions the SCP method locally converges to a KKT point of (P) and the rate of convergence is linear. Problems in the form of ...

We consider the problem of finding decentralized controllers to optimize an H∞norm. This can be cast as a convex optimization problem when certain conditions are satisfied, but it is an infinite-dimensional problem that still cannot be addressed with existing methods. We useQ-parametrization to approach the original problem with a sequence of finite-dimensional problems. A method is discussed t...

This paper studies several classes of nonconvex optimization problems defined over convex cones, establishing connections between them and demonstrating that they can be equivalently formulated as convex completely positive programs. The problems being studied include: a conic quadratically constrained quadratic program (QCQP), a conic quadratic program with complementarity constraints (QPCC), ...

In 2020, Yamakawa and Okuno proposed a stabilized sequential quadratic semidefinite programming (SQSDP) method for solving, in particular, degenerate nonlinear optimization problems. The algorithm is shown to converge globally without constraint qualification, it has some nice properties, including the feasible subproblems, their possible inexact computations. convergence was established approx...

Factor Analysis (FA) is a technique of fundamental importance that is widely used in classical and modern multivariate statistics, psychometrics, and econometrics. In this paper, we revisit the classical rank-constrained FA problem which seeks to approximate an observed covariance matrix (Σ) by the sum of a Positive Semidefinite (PSD) low-rank component (Θ) and a diagonal matrix (Φ) (with nonne...