In uncertainty theory, quadratic entropy is a type of entropy that provide a quantitative measurement of the uncertainty of uncertain variables. This paper presents the maximum entropy principle for quadratic entropy of uncertain variables, that is, out of all the uncertainty distributions satisfying given constraints, choose the maximum quadratic entropy one.

We describe an implementation of an infinite-dimensional primal-dual algorithm based on the Nesterov-Todd direction. Several applications to both continuous and discrete-time multi-criteria linear-quadratic control problems and linear-quadratic control problem with quadratic constraints are described. Numerical results show a very fast convergence (typically, within 3-4 iterations) to optimal s...

A large class of separable quadratic programming problems is presented The problems in the class can be solved in linear time The class in cludes the separable convex quadratic transportation problem with a xed number of sources and separable convex quadratic programming with nonnegativity con straints and a xed number of linear equality constraints

The previously developed Predictive Pole Placement (PPP) controller is modified to give enhanced numerical and stability properties by embedding the method in a linear-quadratic formulation to give a linear-quadratic PPP (LQPPP) controller. Input, output and state constraints are considered using an natural quadratic programming (QP) formulation of LQPPP. Illustrative examples are given. Copyri...

In this paper, we consider minimizing the ratio of two indefinite quadratic functions subject to two quadratic constraints. Using the extension of Charnes– Cooper transformation, we transform the problem to a homogenized quadratic problem. Then, we show that, under certain assumptions, it can be solved to global optimality using semidefinite optimization relaxation.

2 Duality, central trajectories and We develop an interior-point technique for soiving quadratic dynamical systems programming problem in a Hilbert space.As an example we consider an application of these results to the linear-quadratic control problem with linear inequality constraints. It is shown that the Newton step in this situation is basically reduced to solving the standard linear-quadra...

A recurrent neural network is presented for solving systems of quadratic programming problems with equality constraints involving complex-valued coefficients. The proposed recurrent neural network is asymptotically stable and able to generate optimal solutions to quadratic programs with equality constraints. An opamp based analogue circuit realization of the recurrent neural network is describe...

A method is proposed to compute robust inner-approximations the backward reachable set of nonlinear systems and generate a control law that drives trajectories starting in these target set. The merges dissipation inequalities integral quadratic constraints (IQCs) with both hard soft IQC factorizations, allowing for variety perturbations including parametric uncertainty, unmodeled dynamics, unce...