Karmarkar's algorithm

Karmarkar's algorithm is an algorithm introduced by Narendra Karmarkar in 1984 for solving linear programming problems. It was the first reasonably efficient algorithm that solves these problems in polynomial time. The ellipsoid method is also polynomial time but proved to be inefficient in practice.

Where $n$ is the number of variables and $L$ is the number of bits of input to the algorithm, Karmarkar's algorithm requires $O(n^{3.5}L)$ operations on $O(L)$ digit numbers, as compared to $O(n^{6}L)$ such operations for the ellipsoid algorithm. The runtime of Karmarkar's algorithm is thus $O(n^{3.5}L^{2}\ln L\ln \ln L)$ using FFT-based multiplication (see Big O notation).

Karmarkar's algorithm falls within the class of interior point methods: the current guess for the solution does not follow the boundary of the feasible set as in the simplex method, but it moves through the interior of the feasible region and reaches the optimal solution only asymptotically.

The Algorithm

Consider a Linear Programming problem in matrix form:

maximize cTx
subject to	Ax ≤ b.

The algorithm determines the next feasible direction toward optimality and scales back by a factor 0 < γ ≤ 1.

Karmarkar's algorithm is rather complicated. A simplified version, called the affine-scaling method, proposed and analyzed by others, can be described succinctly as follows. Note that the affine-scaling algorithm, while efficient in practice, is not a polynomial time algorithm.

Algorithm Affine-Scaling
  Input:  A, b, c, $x^{0}$, stopping criterion, $\gamma$.

  $k\leftarrow 0$
  do while stopping criterion not satisfied
     $v^{k}\leftarrow b-Ax^{k}$
     $D_{v}\leftarrow \operatorname {diag} (v_{1}^{k},\ldots ,v_{m}^{k})$
     $h_{x}\leftarrow (A^{T}D_{v}^{-2}A)^{-1}c$
     $h_{v}\leftarrow -Ah_{x}$
     if $h_{v}\geq 0$ then
        return unbounded
     end if
     $\alpha \leftarrow \gamma \cdot \min\{-v_{i}^{k}/(h_{v})_{i}\,\,|\,\,(h_{v})_{i}<0,\,i=1,\ldots ,m\}$
     $x^{k+1}\leftarrow x^{k}+\alpha h_{x}$
     $k\leftarrow k+1$
  end do

• "←" denotes assignment. For instance, "largest ← item" means that the value of largest changes to the value of item.
• "return" terminates the algorithm and outputs the following value.

Example

File:Karmarkar.png

Example solution

Consider the linear program

maximize		$x_{1}$	+	$x_{2}$
subject to		$2px_{1}$	+	$x_{2}$	$\leq$ $p^{2}+1$, $p=0.0,0.1,0.2,\ldots ,0.9,1.0.$

That is, there are 2 variables $x_{1},x_{2}$ and 11 constraints associated with varying values of $p$. This figure shows each iteration of the algorithm as red circle points. The constraints are shown as blue lines.

Patent controversy

At the time he discovered the algorithm, Narendra Karmarkar was employed by AT&T and they realized that his discovery could be of practical importance. In April 1985, AT&T promptly applied for a patent on Karmarkar's algorithm and that became more fuel for the ongoing controversy over the issue of software patents. This left many mathematicians uneasy, such as Ronald Rivest (himself one of the holders of the patent on the RSA algorithm), who expressed the opinion that research proceeded on the basis that algorithms should be free. Even before the patent was actually granted, it seemed that there was prior art that might have been applicable. Mathematicians who specialize in numerical analysis such as Philip Gill and others showed that Karmarkar's algorithm is actually equivalent to a projected Newton barrier method with a logarithmic barrier function, if the parameters are chosen suitably. However, some say Gill's argument is flawed, and the method they describe does not even qualify as an "algorithm", since it requires choices of parameters that don't follow from the internal logic of the method,but rely on external guidance, essentially from Karmarkar's algorithm. Methods referred to by Gill were widely used for nonlinear programming since the 1960s. In fact, one well-known book first published in 1968 described the technique specifically in the context of linear programming. Nevertheless, the patent was eventually granted as U.S. patent 4,744,026: "Methods and apparatus for efficient resource allocation" in May 1988. The patent, however, proved to be of limited commercial value to AT&T. They built up the KORBX system, an 8-processor Alliant computer incorporating linear programming software using Karmarkar's algorithm, priced at US$8.9 million each, and unsurprisingly they only managed to sell two such systems. Opponents of software patents have further alleged that the patents ruined the positive interaction cycles that previously characterized the relationship between researchers in linear programming and industry, and specifically it isolated Karmarkar himself from the network of mathematical researchers in his field.

The patent itself expired in April 2006, and the algorithm is presently in the public domain.

References

• Ilan Adler, Narendra Karmarkar, Mauricio G.C. Resende and Geraldo Veiga (1989). "An Implementation of Karmarkar's Algorithm for Linear Programming". Mathematical Programming, Vol 44, p. 297–335.
• Narendra Karmarkar (1984). "A New Polynomial Time Algorithm for Linear Programming", Combinatorica, Vol 4, nr. 4, p. 373–395.