An improved dual simplex algorithm for the solution of the discrete linear Lj approximation problem is described. In this algorithm certain intermediate iterations are skipped. This method is comparable with an improved simplex method due to Barrodale and Roberts, in both speed and number of iterations. It also has the advantage that in case of ill-conditioned problems, the basis matrix can len...

The random simplex algorithm for linear programming proceeds as follows: at each step, it moves from a vertex u of the polytope to a randomly chosen neighbor of u, the random choice being made from those neighbors of u that improve the objective function. We exhibit a polytope defined by n constraints in three dimensions with height O(log n), for which the expected running time of the random si...

We present a new efficient approach for solving the multicommodity flow problem as a sequence of subproblems, each on a very sparse but connected network. We show that each subproblem can be contracted to a problem on a much smaller graph. We then solve these problems using the simplex method. We test the algorithm on benchmark instances, and show its improvement over the usual simplex algorithm.

Generalizations of the well-known simplex method for linear programming are available to solve the piecewise linear programming problem and the linear fractional programming problem. In this paper we consider a further generalization of the simplex method to solve piecewise linear fractional programming problems unifying the simplex method for linear programs, piecewise linear programs, and the...

For many of us, modern-day linear programming (LP) started with the work of George Dantzig in 1947. However, it must be said that many other scientists have also made seminal contributions to the subject, and some would argue that the origins of LP predate Dantzig’s contribution. It is matter open to debate [36]. However, what is not open to debate is Dantzig’s key contribution to LP computatio...

We present pure-integer Gomory cuts in a way so that they are derived with respect to a “dual form” pure-integer optimization problem and applied on the standard-form primal side as columns, using the primal simplex algorithm. The input integer problem is not in standard form, and so the cuts are derived a bit differently. In this manner, we obtain a finitely-terminating version of pure-integer...

In this paper, we develop a novel derivative-free deterministic greedy algorithm for global optimization of any objective function of parameters belonging to a unit-simplex. Main principle of the proposed algorithm is making jumps of varying step-sizes within the simplex parameter space and searching for the best direction to move in a greedy manner. Unlike most of the other existing methods of...

The linear programming problem is usually solved through the use of one of two algorithms: either simplex, or an algorithm in the family of interior point methods. In this article two representative members of the family of interior point methods are introduced and studied. We discuss the design of these interior point methods on a high level, and compare them to both the simplex algorithm and ...

We show that there are simplex pivoting rules for which it is PSPACE-complete to tell if a particular basis will appear on the algorithm’s path. Such rules cannot be the basis of a strongly polynomial algorithm, unless P = PSPACE. We conjecture that the same can be shown for most known variants of the simplex method. However, we also point out that Dantzig’s shadow vertex algorithm has a polyno...

In real optimization problems, usually the analytical expression of the objective function is not known, nor its derivatives, or they are complex. In these cases it becomes essential to use optimization methods where the calculation of the derivatives, or the verification of their existence, is not necessary: the Direct Search Methods or Derivative-free Methods are one solution. When the proble...