Nonlinear network flow problems with linear/nonlinear side constraints can be solved by means of Lagrangian relaxations. The dual problem is the maximization of a dual function whose value is estimated by minimizing approximately a Lagrangian function on the set defined by the network constraints. We study alternative stepsizes in the approximate subgradient methods to solve the dual problem. S...

We consider computing the saddle points of a convex-concave function using subgradient methods. The existing literature on finding saddle points has mainly focused on establishing convergence properties of the generated iterates under some restrictive assumptions. In this paper, we propose a subgradient algorithm for generating approximate saddle points and provide per-iteration convergence rat...

The immersed boundary (IB) method is a framework for modeling systems in which an elastic structure is immersed in a viscous incompressible fluid. The IB formulation of such problems describes the elasticity of the structure in Lagrangian form and describes the momentum, viscosity, and incompressibility of the fluid-structure system in Eulerian form. Interactions between Lagrangian and Eulerian...

This paper presents a method based on nonlinear programming for short term scheduling of hydro power system. The proposed algorithm is based on the solution of an augmented lagrangian function of the scheduling problem using conjugate gradient method. The water transportation delay between cascaded reservoirs is considered. Results concerning this method are compared with those achieved from su...

This paper studies a fundamental bicriteria optimization problem for variable selection in statistical learning; the two criteria are a loss/residual function and a model control (also called regularization, penalty). The former function measures the fitness of the learning model to data and the latter function is employed as a control of the complexity of the model. We focus on the case where ...

This paper proposes an augmented Lagrange Hopfield network (ALHN) for combined heat and power economic dispatch (CHPED) problem. The ALHN method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed ALHN, the energy function is augmented by Hopfield terms from Hopfield neural network and penalty factors from augmented Lagran...

In constrained optimisation, the augmented Lagrangian method is considered as one of the most effective and efficient methods. This paper studies the behaviour of augmented Lagrangian function (ALF) in the solution space and then proposes an improved augmented Lagrangian method. We have shown that our proposed method can overcome some of the drawbacks of the conventional augmented Lagrangian me...

Let L be a time-periodic Tonelli Lagrangian on a closed manifold of dimension two. Then the β-function of L is differentiable in at least k directions at any k-irrational homology class. The same result holds when L is an autonomous mechanical Lagrangian with a C potential on a closed manifold of dimension three.

We present a new derivative-free trust-region (DFTR) algorithm to solve general nonlinear constrained problems with the use of an augmented Lagrangian method. No derivatives are used, neither for the objective function nor for the constraints. An augmented Lagrangian method, known as an effective tool to solve equality and inequality constrained optimization problems with derivatives, is exploi...