AbstractThe paper suggests a new implementation of the active set method for solving linear programming problems. The proposed method is based on the observation that the search direction can be obtained via the solution of a linear least squares subproblem. It is shown that the steepest descent direction can be computed by solving the same least squares subproblem but with simple bounds on the variables. This direction is used to prevent cycling at degenerate dead points. Numerical experiments illustrate the feasibility of the new approach
We propose an algorithm for linear programming, which we call the Sequential Projection algorithm. T...
AbstractThree new iterative methods for the solution of the linear least squares problem with bound ...
We discuss a finite method of a feasible direction for linear programming problems. The method begin...
AbstractThe paper suggests a new implementation of the active set method for solving linear programm...
We will present a potential reduction method for linear programming where only the constraints with ...
We describe how to maintain an explicit sparse orthogonal factorization in order to solve the sequen...
In this paper, we describe a new active-set algorithmic framework for minimizing a non-convex functi...
This paper describes an active-set algorithm for large-scale nonlinear programming based on the succ...
This thesis proposes a new active-set method for large-scale nonlinearly con strained optimization. ...
An algorithm for solving linearly constrained optimization problems is proposed. The search directio...
We analyze an abridged version of the active-set algorithm FPC_AS for solving the L1-regularized lea...
It is now well established that, especially on large linearprogramming problems, the simplex method ...
For solving nonlinear optimization problems, two competing iterative approaches are available: activ...
The problem of finding sparse solutions to underdetermined systems of linear equations arises in sev...
We propose a numerical algorithm for solving smooth nonlinear programming problems with a large numb...
We propose an algorithm for linear programming, which we call the Sequential Projection algorithm. T...
AbstractThree new iterative methods for the solution of the linear least squares problem with bound ...
We discuss a finite method of a feasible direction for linear programming problems. The method begin...
AbstractThe paper suggests a new implementation of the active set method for solving linear programm...
We will present a potential reduction method for linear programming where only the constraints with ...
We describe how to maintain an explicit sparse orthogonal factorization in order to solve the sequen...
In this paper, we describe a new active-set algorithmic framework for minimizing a non-convex functi...
This paper describes an active-set algorithm for large-scale nonlinear programming based on the succ...
This thesis proposes a new active-set method for large-scale nonlinearly con strained optimization. ...
An algorithm for solving linearly constrained optimization problems is proposed. The search directio...
We analyze an abridged version of the active-set algorithm FPC_AS for solving the L1-regularized lea...
It is now well established that, especially on large linearprogramming problems, the simplex method ...
For solving nonlinear optimization problems, two competing iterative approaches are available: activ...
The problem of finding sparse solutions to underdetermined systems of linear equations arises in sev...
We propose a numerical algorithm for solving smooth nonlinear programming problems with a large numb...
We propose an algorithm for linear programming, which we call the Sequential Projection algorithm. T...
AbstractThree new iterative methods for the solution of the linear least squares problem with bound ...
We discuss a finite method of a feasible direction for linear programming problems. The method begin...