We consider a wireless sensor network's lifetime maximization problem as an integer linear programming problem when the redundant set of covers is given, and it is necessary to choose a lifetime of each cover subject to the limited sensor's resources. Irrespective of the function of any sensor...
The linear programming problem can be solved using different methods, such as the graphical method, simplex method, or by using tools such as R, open solver etc. Here, we will discuss thetwomost important techniques called the simplex method and graphical method in detail. What is maximization ...
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 ...
Weighted determinant maximization with linear matrix inequality constraints (maxdet-problem) is a generalization of the semidefinite programming. We give a... Xia,Yu - 《Optimization Methods & Software》 被引量: 1发表: 2008年 Consensus Maximization with Linear Matrix Inequality Constraints Weighted dete...
Given a graph G = (V, E), a weight function w: E rarr R + , and a parameter k, we consider the problem of finding a subset U sube V of size k that maximizes: Max-Vertex Cover k the weight of edges incident with vertices in U, Max-Dense Subgraph k the weight of edges in ...
Linear and integer programming problems with Lingo and Gusek Posing and solving three optimization problems: The first problem deals with profit maximization in an agricultural production plan with limited land and water. The basic syntax for defining variables and constraints is shown. The second prob...
of the problem’s characteristic length scale. (Or, you could have different λ i ’s for each vector direction.) The downhill simplex method nowtakes a series of steps, most steps just moving the point of the simplex where the function is largest (“highest point”) through the opposite...
. For problem-based optimization you define your variables as symbolic-type arrays of any dimension, and your objective and constraints as linear functions of those variables. See the examples in
In this paper, by jointly considering subchannels, power, and Modulation and Coding Scheme (MCS) allocation, we address the sum-rate maximization problem in OFDMA downlink systems. We formulate the problem as an integer linear programming (ILP), which maximizes the system sum-rate subject to the...
A Linear Programming Approach to Max-Sum Problem: A Review The max-sum labeling problem, defined as maximizing a sum of binary (i.e., pairwise) functions of discrete variables, is a general NP-hard optimization pro... T Werner - 《IEEE Transactions on Pattern Analysis & Machine Intelligence...