A new Lagrangian net algorithm for solving max-bisection problems

Discrete Lagrangian-Based Search for Solving MAX-SAT Problems

Weighted maximum satissability problems (MAX-SAT) are diicult to solve due to the large number of local minima in their search space. In this paper we propose a new discrete Lagrangian based search method (DLM) for solving these problems. Instead of restarting from a new point when the search reaches a local minimum, the Lagrange multipliers in DLM provide a force to lead the search out of the ...

A .699-approximation algorithm for Max-Bisection

An Interior Point Algorithm for Solving Convex Quadratic Semidefinite Optimization Problems Using a New Kernel Function

In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) fun...

A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems

Global optimization methods play an important role to solve many real-world problems. Flower pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new hybrid optimization method called hybrid flower pollination algorithm (FPPSO) is proposed. The method combines the standard flower pollination algorithm (FP) with the par...

A New Method for Solving Constraint Satisfaction Problems

Many important problems in Artificial Intelligence can be defined as Constraint Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constraints on the values of those variables (these problems are also called Consistent Labeling Problems (CLP), in which “Labeling" means assigning a value to a variable.) Sol...