The current work focuses on the development and application of an efficient algorithm for optimization of threedimensional turbulent flows, simulated using Direct Numerical Simulation (DNS) or large-eddy simulations, and further characterized by large-dimensional optimization-parameter spaces. The optimization algorithm is based on Sequential Quadratic Programming (SQP) in combination with a da...

This paper presents an extensible framework for designing analog filters that exhibit several desired behavioral properties after being realized in circuits. In the framework, we model the constrained nonlinear optimization problem as a sequential quadratic programming (SQP) problem. SQP requires real-valued constraints and objective functions that are differentiable with respect to the free pa...

Transpose-and-Cache Branch-and-Bound (TCBB) has shown promise in solving large single machine quadratic penalty problems. There exist other classes of single machine job sequencing problems which are of more practical importance and which are also of considerable interest in the area of AI search. In the weighted earliness tardiness problem (WET), the best known heuristic estimate is not consis...

Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding th...

We propose a sequential quadratic programming (SQP) method for the optimal control of large-scale dynamical systems. The method uses modified multiple shooting to discretize the dynamical constraints. When these systems have relatively few parameters, the computational complexity of the modified method is much less than that of standard multiple shooting. Moreover, the proposed method is demons...

The control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. There are varieties of problems in chemical engineering that can be formulated as NonLinear Programming (NLPs). The quality of the developed solution significantly affects the performance ...

This paper discusses nonlinear optimization techniques in robust control synthesis, with special emphasis on design problems which may be cast as minimizing a linear objective function under linear matrix inequality (LMI) constraints in tandem with nonlinear matrix equality constraints. The latter type of constraints renders the design numerically and algorithmically difficult. We solve the opt...

An algorithm for general nonlinearly constrained optimization is presented, which solves an unconstrained piecewise quadratic subprob-lem and a quadratic programming subproblem at each iterate. The algorithm is robust since it can circumvent the diiculties associated with the possible inconsistency of QP subproblem of the original SQP method. Moreover, the algorithm can converge to a point whic...