We develop a Newton method for the optimization of trajectory functionals. Through the use of a trajectory tracking nonlinear projection operator, the dynamically constrained optimization problem is converted into an unconstrained problem, making many aspects of the algorithm rather transparent. Examples: first and second order optimality conditions, search direction and step length calculation...

In this paper, we address a class of distributed optimization problems in the presence of inter-agent communication delays based on passivity. We first focus on unconstrained distributed optimization and provide a passivity-based perspective for distributed optimization algorithms. With the help of the scattering transformation, this perspective allows us to handle arbitrary and unknown constan...

A new active set algorithm (ASA) for large-scale box constrained optimization is introduced. The algorithm consists of a nonmonotone gradient projection step, an unconstrained optimization step, and a set of rules for switching between the two steps. Numerical experiments and comparisons are presented using box constrained problems in the CUTEr and MINPACK test problem libraries. keywords: Nonm...

This paper extends the Weighted Low Rank Approximation (WLRA) approach towards linearly structured matrices. In the case of Hankel matrices with a special block structure an equivalent unconstrained optimization problem is derived and an algorithm for solving it is proposed.

An overview of interval arithmetical tools and basic techniques is presented that can be used to construct deterministic global optimization algorithms. These tools are applicable to unconstrained and constrained optimization as well as to nonsmooth optimization and to problems over unbounded domains. Since almost all interval based global optimization algorithms use branch-and-bound methods wi...

We discuss the energy generation expansion planning with environmental constraints, formulated as a nonsmooth convex constrained optimization problem. To solve such problems, methods suitable for constrained nonsmooth optimization need to be employed. We describe a recently developed approach, which applies the usual unconstrained bundle techniques to a dynamically changing “improvement functio...

The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: the multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that t...

A novel value-estimation function method for global optimization problems with inequality constraints is proposed in this paper. The value-estimation function formulation is an auxiliary unconstrained optimization problem with a univariate parameter that represents an estimated optimal value of the objective function of the original optimization problem. A solution is optimal to the original pr...

This paper presents the numerical solution of a constrained optimal control problem (COCP) for quasilinear parabolic equations. The COCP is converted to unconstrained optimization problem (UOCP) by applying the exterior penalty function method. Necessary optimality conditions for the considered problem are established. The computing optimal controls are helped to identify the unknown coefficien...

In this paper, we formulate neural-network training as a constrained optimization problem instead of the traditional formulation based on unconstrained optimization. We show that constraints violated during a search provide additional force to help escape from local minima using our newly developed constrained simulated annealing (CSA) algorithm. We demonstrate the merits of our approach by tra...