Sentence examples for mixed complementarity problem from inspiring English sources
This paper presents two feedback neural networks for solving a nonlinear and mixed complementarity problem.
The governing formulation is cast in mixed static-kinematic variables and leads naturally to what is known in the mathematical programming literature as a mixed complementarity problem (MCP).
These adjustable products are modeled within the context of the European electricity day-ahead market problem, which is formulated as two different mathematical programming models: a Mixed Complementarity Problem (MCP) and a Linear Programming model (LP).
A Mixed Complementarity Problem is utilized for clearing the market, in which the demand clearing prices are implicitly defined by mixing the explicit prices for energy, reserves and the RES uplift.
A stochastic mixed complementarity problem (MCP) is used to model the interactions between the firms, and we apply this methodology to a test system using Irish electricity market data.
This is achieved by augmenting the set of complementary reactive power relations at voltage controlled buses with the conventional set of active power balance equations at all buses and reactive power balance equations at load buses in a Mixed Complementarity Problem (MCP) framework.
Sun, Robert, and Qi have proposed a feasible semi-smooth asymptotically Newton method for mixed complementarity problems [4].
As for SSEA and TLASM, some modifications are needed in order to solve the mixed complementarity problems.
In this paper, we present some finite algorithms to the numerical solution of almost linear (mixed) complementarity problems.
In Section 4, semi-iterative algorithms are proposed for solving upper obstacle problems and mixed complementarity problems, respectively, via their PALS reformulations, and the monotone and finite convergence of the algorithms are also obtained.
So, we only run Algorithm 4.1 for Problem 4 and the iteration numbers are listed in Table 4 to solve the mixed complementarity problems with difference dimensions discretized by Problem 4.
