DCA-based algorithms for DC fitting

DC Programming and DCA for Nonnegative Matrix Factorization

Improving Simulation-Based Algorithms for Fitting ERGMs.

Markov chain Monte Carlo methods can be used to approximate the intractable normalizing constants that arise in likelihood calculations for many exponential family random graph models for networks. However, in practice, the resulting approximations degrade as parameter values move away from the value used to define the Markov chain, even in cases where the chain produces perfectly efficient sam...

DC programming and DCA for enhancing physical layer security via cooperative jamming

The explosive development of computational tools these days is threatening security of cryptographic algorithms those are regarded as primary traditional methods for ensuring information security. Physical layer security approach is introduced as a method for both improving confidentiality of the secret key distribution in cryptography and enabling the data transmission without relaying on high...

DC Programming and DCA for Portfolio Optimization with Linear and Fixed Transaction Costs

We consider the problem of portfolio selection, with transaction costs and constraints on exposure to risk. Linear transaction costs, bounds on the variance of the return, and bounds on different shortfall probabilities are efficiently handled by convex optimization methods. Problems with transaction costs that include a fixed fee, or discount breakpoints, cannot be directly solved by convex op...

Multi-Stage DC-AC Converter Based on new DC-DC converter for energy conversion

This paper proposes a multi-stage power generation system suitable for renewable energy sources, which is composed of a DC-DC power converter and a three-phase inverter. The DC-DC power converter is a boost converter to convert the output voltage of the DC source into two voltage sources. The DC-DC converter has two switches operates like a continuous conduction mode. The input current of DC-DC...