49,045 research outputs found
Global Optimization for a Class of Nonlinear Sum of Ratios Problem
We present a branch and bound algorithm for globally solving the sum of ratios problem. In this problem, each term in the objective function is a ratio of two functions which are the sums of the absolute values of affine functions with coefficients. This problem has an important application in financial optimization, but the global optimization algorithm for this problem is still rare in the literature so far. In the algorithm we presented, the branch and bound search undertaken by the algorithm uses rectangular partitioning and takes place in a space which typically has a much smaller dimension than the space to which the decision variables of this problem belong. Convergence of the algorithm is shown. At last, some numerical examples are given to vindicate our conclusions
Recommended from our members
Optimal exact designs of experiments via Mixed Integer Nonlinear Programming
Optimal exact designs are problematic to find and study because there is no unified theory for determining them and studyingtheir properties. Each has its own challenges and when a method exists to confirm the design optimality, it is invariablyapplicable to the particular problem only.We propose a systematic approach to construct optimal exact designs by incorporatingthe Cholesky decomposition of the Fisher Information Matrix in a Mixed Integer Nonlinear Programming formulation. Asexamples, we apply the methodology to find D- and A-optimal exact designs for linear and nonlinear models using global orlocal optimizers. Our examples include design problems with constraints on the locations or the number of replicates at theoptimal design points
Power Allocation and Scheduling for SWIPT Systems with Non-linear Energy Harvesting Model
In this paper, we design a resource allocation algorithm for multiuser
simultaneous wireless information and power transfer systems for a realistic
non-linear energy harvesting (EH) model. In particular, the algorithm design is
formulated as a non-convex optimization problem for the maximization of the
long-term average total harvested power at EH receivers subject to quality of
service requirements for information decoding receivers. To obtain a tractable
solution, we transform the corresponding non-convex sum-of-ratios objective
function into an equivalent objective function in parametric subtractive form.
This leads to a computationally efficient iterative resource allocation
algorithm. Numerical results reveal a significant performance gain that can be
achieved if the resource allocation algorithm design is based on the non-linear
EH model instead of the traditional linear model.Comment: Accepted for presentation at the IEEE ICC 201
- …