The design of an efficient energy management system (EMS) for monopolar DC networks with high penetration photovoltaic generation plants is addressed in this research through a convex optimization point view. EMS formulated as multi-objective problem that involves economic, technical, and environmental objective functions subject to typical constraints regarding power balance equilibrium, therm...

Widespread application of dynamic optimization with fast optimization solvers leads to increased consideration of first-principles models for nonlinear model predictive control (NMPC). However, significant barriers to this optimization-based control strategy are feedback delays and consequent loss of performance and stability due to on-line computation. To overcome these barriers, recently prop...

Dynamic traffic assignment (DTA) models can be realized with vehicle-based simulation approach or flow-based analytical approach. Depending on methodology, the flow-based analytical models can be further classified into mathematical programming, optimal control and variational inequality models. A brief review of the development of analytical models is given in this paper. A stochastic dynamic ...

A strategy based on Nonlinear Programming (NLP) sensitivity is developed to establish stability bounds on the plant/model mismatch for a class of optimization-based Model Predictive Control (MPC) algorithms. By extending well-known nominal stability properties for these controllers, we derive a sucient condition for robust stability of these controllers. This condition can also be used to asse...

This paper introduces a derivative-free and ready-to-use solver for nonlinear programs with nonlinear equality and inequality constraints (NLPs). Using finite differences and a sequential quadratic programming (SQP) approach, the algorithm aims at finding a local minimizer and no extra attempt is made to generate a globally optimal solution. Due to the use of finite differences, approximations ...

We develop a 0-1 non-linear programming (NLP) model for combined temporal partitioning and high-level synthesis from behavioral speciications destined to be implemented on reconngurable processors. We present tight linearizations of the NLP model. We present eeective variable selection heuristics for a branch and bound solution of the derived linear programming model. We show how tight lineariz...

We present an interior-point penalty method for nonlinear programming (NLP), where the merit function consists of a piecewise linear penalty function (PLPF) and an `2-penalty function. The PLPF is defined by a set of penalty parameters that correspond to break points of the PLPF and are updated at every iteration. The `2-penalty function, like traditional penalty functions for NLP, is defined b...

We study mathematical programs with linear complementarity constraints (MPLCC) for which the objective function is smooth. Current nonlinear programming (NLP) based algorithms including regularization methods and decomposition methods generate only weak (e.g., Cor M-) stationary points that may not be first-order solutions to the MPLCC. Piecewise sequential quadratic programming methods enjoy s...

Parallel programming is becoming increasingly popular. Computers have increasingly many cores (processors). Also, large computer-clusters are becoming available. But there is still no good programming framework for these architectures, and thus no simple and unified way for NLP applications to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel prog...

Natural language processing (NLP) is a sub-area of artificial intelligence that deals with human interaction with the machine. There are many tools available for natural language processing in many platforms. Each toolkit can be used with a different programming language. So users can choose different toolkits to work on NLP depending on their familiarity with a particular programming language....