1 Overview We survey the basic notions of cones and cone-LP and give several examples mostly related to semidefinite programming. The linear and semidefinite programming problems are formulated as follows: Let c ∈ n and b ∈ m ,A ∈ n×m with rows a i ∈

When solving the linear programming (LP) relaxation of a mixed-integer program (MIP) with column generation, columns might be generated that are not needed to express any integer optimal solution. Such called strongly redundant and dual bound obtained by LP is potentially stronger if these generated. We introduce sufficient condition for strong redundancy can checked compact LP. Using solution ...

In automatic speech recognition, the decoding parameters — grammar factor and word insertion penalty — are usually handtuned to give the best recognition performance. This paper investigates an automatic procedure to determine their values using an iterative linear programming (LP) algorithm. LP naturally implements discriminative training by mapping linear discriminants into LP constraints. A ...

Maximum a posteriori (MAP) inference is one of the fundamental inference tasks in graphical models. MAP inference is in general NP-hard, making approximate methods of interest for many problems. One successful class of approximate inference algorithms is based on linear programming (LP) relaxations. The augmented Lagrangian method can be used to overcome a lack of strict convexity in LP relaxat...

A strategy is described for using linear programming (LP) to bound the solution set of the linear interval equation system that must be solved in the context of the interval-Newton method for deterministic global optimization. An implementation of this technique is described in detail, and several important issues are considered. These include selection of the interval corner required by the LP...

This paper presents linear programming (LP) formulations for short-term energy time-shift operational scheduling with energy storage systems (ESSs) in power grids. In particular, it is shown that the conventional nonlinear formulations for electric bill minimization, peak shaving, and load leveling can be formulated in the LP framework. New variables for the peak and off-peak values are introdu...

We consider statistical procedures for feature selection defined by a family of regularization problems with convex piecewise linear loss functions and penalties of l1 nature. Many known statistical procedures (e.g. quantile regression and support vector machines with l1 norm penalty) are subsumed under this category. Computationally, the regularization problems are linear programming (LP) prob...

The paper introduces a novel, scalable control allocation (CA) formulation for active capacitor balancing of the Modular Multilevel Converter (MMC). is obtained by introducing converter arm model that also and accommodates an MMC with any number phases submodules (SM). CA algorithm involves fast real-time minimization error cost function formalized as linear programming (LP) quadratic (QP) prob...

1. Introduction This document is the rst year progress report on the optimization projects funded by NSF Grant DDM-8922636. The projects principally include the interior-point algorithms for linear programming (LP), quadratic programming (QP), linear complementarity problem (LCP), and nonlinear programming (NP). The anticipated discoveries and advances resulting from the project include the fol...