The efficient solving of many CSPs has been achieved by hybrid methods that combine Constraint Programming (CP) and Linear Programming (LP). One such case is the ‘Maximum Density Still Life’ problem. An obstacle to overcome in order to solve this problem efficiently is the inherent symmetry. Symmetry in Constraint Satisfaction Problems (CSPs) can lead to redundant search, since subtrees may be ...

In this paper, we develop a computer technique to implement the existing 2-basic variable replacement method of Paranjape for solving linear programming (LP) problems. To our knowledge there is no such computer oriented program which implemented Paranjape’s method. Our computer oriented program is a faster method for solving linear programs. A number of numerical examples are illustrated to dem...

In this paper, a new algorithm for Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations. The solution obtained by the proposed algorithm is compared with the minimum -norm solution achieved by Linear Programming (LP). It ...

Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typicall...

This paper describes a linear programming (LP) formulation applicable to the timing analysis of large scale SOC synchronous circuits with level-sensitive latches. The proposed formulation uses a variation of the big M method [1] to modify the nonlinear constraints in the problem formulation into solvable linear constraints. By making maximum use of cycle stealing [2], operation at a higher cloc...

In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems of linear equations, and its applications include underdetermined Sparse Component Analysis (SCA), atomic decomposition on overcomplete dictionaries, compressed sensing, and decoding real £eld codes. Contr...

Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity relationship (QSAR) analysis, the central task is to find a regression function that predicts the activity of the molecule in high accuracy. Setting a QSAR as a primal target, we propose a new linear programming approach to the graph-based regression problem. Our method extends the graph classificati...

We propose a new metaheuristic called heuristic concentration-integer (HCI). This metaheuristic is a modified version of the heuristic concentration (HC), oriented to find good solutions for a class of integer programming problems, composed by problems in which p elements must be selected from a larger set, and each element can be selected more than once. These problems are common in location a...

due to increasing demand for the scarce available water throughout the world it is an extremely important matter, in water management, to make serious attempts in determining its true economic value. this paper discusses the optimal allocation of water to agriculture, the relatively true economic value of water as well as the cropping patterns for the shirvan barzo (sb) dam area in north kh...

Structured prediction applications often involve complex inference problems that require the use of approximate methods. Approximations based on linear programming (LP) relaxations have proved particularly successful in this setting, with both theoretical and empirical support. Despite the general intractability of inference, it has been observed that in many real-world applications the LP rela...