We present a closed-form solution for convex Nonlinear Programming (NLP). It is closed-form solution if all the constraints are linear, quadratic, or homogeneous. It is polynomial when applied to convex NLP. It gives exact optimal solution when applied to LP. The T-forward method moves forward inside the feasible region with a T-shape path toward the increasing direction of the objective functi...

In order to maintain the security of power system at an appropriate level and at low cost, it is essential to accurately assess the steady-state stability limits and power flow feasibility boundaries, i.e., the power system marginal states (MS). This paper is devoted to creation and theoretical substantiation of the MS model based on nonlinear programming (NLP-MS model), its research to reveal ...

In this paper, a general multiperiod nonlinear optimization model is presented, which incorporates synthesis, design, and operation, and takes into account the corresponding benefits and costs in each time period. The model is formulated as a non linear programming (NLP) model in which plant structure decisions are modeled in terms of a superstructure embedded in the overall model. This approac...

Given a partial symmetric matrix A with only certain elements speciied, the Eu-clidean distance matrix completion problem (EDMCP) is to nd the unspeciied elements of A that make A a Euclidean distance matrix (EDM). In this paper, we follow the successful approach in 20] and solve the EDMCP by generalizing the completion problem to allow for approximate completions. In particular, we introduce a...

This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical (ISpS). The proposed algorithm employs parametric nonlinear programming (NLP) sensitivity terminal ingredients to determine minimum stabilizing prediction all scenarios consid...

Natural Language Processing holds great promise for making computer interfaces that are easier to use for people, since people will (hopefully) be able to talk to the computer in their own language, rather than learn a specialized language of computer commands. For programming, however, the necessity of a formal programming language for communicating with a computer has always been taken for gr...

In this paper we propose a multiperiod nonlinear programming (NLP) formulation that incorporates empirical process models for the optimal planning of a multi-plant production site. Using as a basis a real world application of a polymer plant that produces 27 products, a model is developed for predicting the detailed production using actual plant data. The empirical process models account for ra...

Chemical process models are highly structured. Information on how the hierarchical components are connected helps to solve the model efficiently. Our ultimate goal is to develop structure-driven optimization methods for solving nonlinear programming problems (NLP). The structural information retrieved from the JModelica environment will play an important role in the development of our novel opt...

Based on the analysis of the properties of tent map, parallel chaos optimization algorithm (PCOA) using logistic map and outlook algorithm, a hybrid global optimization algorithm (PCOOA) is presented. The algorithm is structured in two stages. The first stage uses parallel chaos optimization based on tent map for global search, while outlook algorithm is employed in the second stage for local s...