two-level stochastic linear programming problemchance constraintprobability maximization modelIn this paper, considering the two-level linear programming problem, which is a numerical model for the optimization of a hierarchical system by two decisionmakers, we focus on the situation where the coefficients...
We present a new approach called MLPR (matrix multiplication, linear programming, randomized rounding) with linear programming used as its core in order to solve the influence maximization problem in the linear threshold model. Experiments on four real data sets have shown the efficiency of the ...
linear programmingThis paper presents a new concept of efficient solution for the linear vector maximization problem. Briefly, these solutions are efficient with respect to the constraints, in addition to being efficient with respect to the multiple objectives. The duality theory of linear vector ...
Atul-Anand-Jha / Optimization-LinearProgramming-Python Star 13 Code Issues Pull requests To implement Optimization (maximization) problem through Linear programming in Python Language. machine-learning jupyter-notebook python3 scipy matplotlib pulp optimization-algorithms maximization Updated Nov 20, 2019...
linear fractional programmingefficient pointsA vector maximum problem (VMP) with linear fractional objectives and non-linear constraints is considered and a condition is derived which is both necessary and sufficient for an efficient solution of (VMP). This study subsumes the results of Isekmann [3...
(1) A Linear Matrix Inequality (LMI) is the constraint on y ∈ IRn such that A(y) 0. A Semi-definite Pro- gram (SDP) consists of minimizing (or maximizing) a lin- ear objective subject to LMI constraints. It is a convex optimization problem that can be efficiently solved using ...
The conversion from d2 to lf2 was possible because of the observation-by-observation nature of the linear-regression problem; if the evaluator was not going to be implemented as lf, it always should have been implemented as lf1 or lf2 instead of d1 or d2. In the d2 evaluator above, ...
To minimize energy consumption with respect to energy dissipationPdiss, the optimization problem is formulated as follows Pdiss≥p{Etx+Erx}. (6) Equation5is defined as the objective function, which is to minimize energy consumption for a linear array of nodes [22]. The two variables that must...
semi-definite programmingcutting plane methodIn this paper, we consider indefinite quadratic maximization problems over inequality constraints. Through the Reformulation and Linearization Technique (RLT), we reformulate the problem as a linear maximization problem over a region which is given by the convex...
Three heuristics are presented for this problem. The first heuristic is an improved version of an already existing heuristic. Other two heuristics are based on column generation and utilize a linear programming solver to solve the master problem, whereas a genetic algorithm is used to solve the ...