We present the moment cone (MC) relaxation and a hierarchy of sparse LagrangianSDP relaxations of polynomial optimization problems (POPs) using the unified framework established in Part I. The MC relaxation is derived for a POP of minimizing a polynomial subject to a nonconvex cone constraint and polynomial equality constraints. It is an extension of the completely positive programming relaxati...

We propose an efficient computational method for linearly constrained quadratic optimization problems (QOPs) with complementarity constraints based on their Lagrangian and doubly nonnegative (DNN) relaxation and first-order algorithms. The simplified Lagrangian-CPP relaxation of such QOPs proposed by Arima, Kim, and Kojima in 2012 takes one of the simplest forms, an unconstrained conic linear o...

We consider global convergence properties of the augmented Lagrangian methods on problems with degenerate constraints, with a special emphasis on mathematical programs with complementarity constraints (MPCC). In the general case, we show convergence to stationary points of the problem under an error bound condition for the feasible set (which is weaker than constraint qualifications), assuming ...

We present the moment cone (MC) relaxation and a hierarchy of sparse LagrangianSDP relaxations of polynomial optimization problems (POPs) using the unified framework established in Part I. The MC relaxation is derived for a POP of minimizing a polynomial subject to a nonconvex cone constraint and polynomial equality constraints. It is an extension of the completely positive programming relaxati...

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

In this paper spectral Galerkin approximation of optimal control problem governed by fractional advection diffusion reaction equation with integral state constraint is investigated. First order condition the discussed. Weighted Jacobi polynomials are used to approximate and adjoint state. A priori error estimates for control, state, Lagrangian multiplier derived. Numerical experiment carried ou...

We develop a new approach towards error control and adaptivity for nite element discretizations in optimization problems governed by partial diierential equations. Using the Lagrangian formalism the goal is to compute stationary points of the rst-order necessary opti-mality conditions. The mesh adaptation is driven by residual-based a posteriori error estimates derived by duality arguments. Thi...

In video coding standards, such as MPEG-4 and H.263, one important question is how to determine motion vectors for motion compensation in the INTER mode. Usually the sum of absolute differences (SAD) or the sum of squared differences (SSD) is employed as a matching criterion. Although these criteria are related to the distortion, they do not consider the bit rate appropriately. If we want to co...

We develop an algorithm for the linear ordering problem, which has a large number of applications such as triangulation of input-output matrices, minimizing total weighted completion time in one-machine scheduling, and aggregation of individual preferences. The algorithm is based on the Lagrangian relaxation of a binary integer linear programming formulation of the problem. Since the number of ...

In this paper, we propose an optimal quadtree segmentation of an image for variable blocksize vector quantization(VBVQ) such that the total distortion of the reconstructed image is minimal and the total required bits don't exceed the bit budget. The above constrain problem is converted into an equivalent unconstrained problem by using Lagrange multiplier. We prune the full quadtree by comparing...