Linear Programming for Cost MinimizationDonghyun Oh
solutions of a parametric nonconvex nondifferentiable optimization problem. motivated by classical parametric quadratic programming theory and reinforced by modern statistical learning studies, both casting an exponential perspective in fully describing such solution paths, we also aim to address the question ...
Then with probability at least , the solution of (1.2) with any satisfies as long as given in (2.21). The constants and are Remark 1.3. It is natural to extend the model (1.1) and model (1.2) to the low rank matrix recovery problem or the signals which are sparse under some ...
We survey the conceptual framework and several applications of the iterative compression technique introduced in 2004 by Reed, Smith, and Vetta. This technique has proven very useful for achieving a number of recent breakthroughs in the development of fixed-parameter algorithms for NP-hard minimization...
This work proposes an approach for the minimization of the Gibbs free energy using linear programming that guarantees finding the global optimum within some level of precision, for any kind of thermodynamic model. The strategy was used in the calculation of chemical and phase equilibrium involving ...
The ReHLine solver has four appealing "linear properties": It applies to any convex piecewise linear-quadratic loss function, including the hinge loss, the check loss, the Huber loss, etc. In addition, it supports linear equality and inequality constraints on the parameter vector. The optimizatio...
contaminationbasedonlinearprogramming CHENGHuanong,MaoWenfeng,ZHENGShiing (ResearchCenterforComputerandChemicalEngineering,OingdaoUniersityof ScienceandTechnology,Oingdao266042,Shandong,China) Abstract:AmethodbasedonIinearprogrammingwasproposedtodesignwaternetworkinbatchchemicaIprocesses ...
The minimization problem in (4.121) can be accomplished practically by reformulating it as a linear programming problem. This reformulation consists of constructing two new vectors from X̃, X̃+ and X̃−, whose components are only positive. The components ofX̃+ are defined as X̃i...
A surface reconstruction technique based on the L 1- minimization of the variation of the gradient is introduced. This leads to a non-smooth convex programming problem. Well-posedness and convergence of the method is established and an in
Windheuser, T., Schlickewei, U., Schmidt, F.R., Cremers, D.: Geometrically consistent elastic matching of 3d shapes: A linear programming solution. In: 2011 International Conference on Computer Vision, IEEE, pp. 2134–2141 (2011) Wang, H., Yang, Y.: Descent methods for elastic body ...