Weights andLlogL

Weights in block iterative methods

In this paper we introduce a sequential block iterative method and its simultaneous version with op-timal combination of weights (instead of convex combination) for solving convex feasibility problems.When the intersection of the given family of convex sets is nonempty, it is shown that any sequencegenerated by the given algorithms converges to a feasible point. Additionally for linear feasibil...

Multiplicative Weights

In this lecture, we will study various applications of the theory of Multiplicative Weights (MW). In this section, we briefly review the general version of the MW algorithm that we studied in the previous lecture. The following sections then show how the theory can be applied to approximately solve zero-sum games and linear programs, and how it connects with the theory of boosting and approxima...

Minoan weights

Quantum-Inspired Neural Network with Quantum Weights and Real Weights

To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer a...

A Fuzzy TOPSIS Approach with Subjective Weights and Objective Weights

Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM which are methods to systematically evaluate alternatives, we propose a new Fuzzy TOPSIS for evaluating outsourcing software companies both using subjective and objective weights. While crisp data are inadequate to m...