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**A new polynomial-time algorithm for linear programming.**
*(English)*
Zbl 0557.90065

This paper discusses a new polynomial time algorithm for linear programming (LP). It is an interior point method whose worst case computational complexity is \(0(n^{3.5}L)\) arithmetic operations on 0(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The complexity bound for this algorithm is better than that for the ellipsoid algorithm by a factor of \(0(n^{2.5}).\)

The author shows that every LP can be transformed into the form: min cx subject to \(x\in \Omega \cap \Delta\), where \(\Omega\) is the subspace \(\{\) \(x: Ax=0\}\) and \(\Delta\) is the simplex \(\{\) \(x: x\geq 0\) and \(\Sigma x_ j=1\}\), and the minimum objective value in the problem is known to be zero. His algorithm solves the LP in this form.

Let \(a_ 0=(1/n)e\), where e is the vector of all 1’s in \(R^ n\). Let \(B(a_ 0,r)\), \(B(a_ 0,R)\) be respectively the largest sphere with center \(a_ 0\) lying in \(\Delta\), and the smallest sphere with center \(a_ 0\) containing \(\Delta\). Then \(R/r=(n-1)\). Using this he shows that if \(a_ 0\) is feasible, \(a_ 0-r\hat c\), where \(\hat c\) is the normalized vector which in the orthogonal projection of c in \(\Omega\), is chosen to the minimum objective value by a factor of (1-1/(n-1)). This is the main result on which the algorithm is based.

The algorithm is initiated with a feasible solution \(x^ 0>0\), and it generates a descent sequence of positive feasible points \(x^ 0,x^ 1,..\).. In the kth step, the point \(x^ k\) is brought into the center of the simplex by a projective transformation, a step of the form described above is taken, and the inverse projective transformation is applied, leading to the next point \(x^{k+1}\), reducing the objective function value by a factor of 0(n). The sequence of points generated, converges to a near optimal solution in polynomial time.

The author shows that every LP can be transformed into the form: min cx subject to \(x\in \Omega \cap \Delta\), where \(\Omega\) is the subspace \(\{\) \(x: Ax=0\}\) and \(\Delta\) is the simplex \(\{\) \(x: x\geq 0\) and \(\Sigma x_ j=1\}\), and the minimum objective value in the problem is known to be zero. His algorithm solves the LP in this form.

Let \(a_ 0=(1/n)e\), where e is the vector of all 1’s in \(R^ n\). Let \(B(a_ 0,r)\), \(B(a_ 0,R)\) be respectively the largest sphere with center \(a_ 0\) lying in \(\Delta\), and the smallest sphere with center \(a_ 0\) containing \(\Delta\). Then \(R/r=(n-1)\). Using this he shows that if \(a_ 0\) is feasible, \(a_ 0-r\hat c\), where \(\hat c\) is the normalized vector which in the orthogonal projection of c in \(\Omega\), is chosen to the minimum objective value by a factor of (1-1/(n-1)). This is the main result on which the algorithm is based.

The algorithm is initiated with a feasible solution \(x^ 0>0\), and it generates a descent sequence of positive feasible points \(x^ 0,x^ 1,..\).. In the kth step, the point \(x^ k\) is brought into the center of the simplex by a projective transformation, a step of the form described above is taken, and the inverse projective transformation is applied, leading to the next point \(x^{k+1}\), reducing the objective function value by a factor of 0(n). The sequence of points generated, converges to a near optimal solution in polynomial time.

Reviewer: K.G.Murty

### Keywords:

polynomial time algorithm; interior point method; computational complexity; near optimal solution
Full Text:
DOI

### References:

[1] | H. S. M. Coxeter,Introduction to Geometry, Wiley (1961). |

[2] | G. B. Dantzig,Linear Programming and Extensions, Princeton University Press, Princeton, NJ (1963). |

[3] | M. Grötschel, L. Lovász andA. Schrijver, The Ellipsoid Method and its Consequences in Combinatorial Optimization,Combinatorica 1 (1981), 169–197. · Zbl 0492.90056 · doi:10.1007/BF02579273 |

[4] | L. G. Khachiyan, A polynomial Algorithm in Linear Programming,Doklady Akademii Nauk SSSR 244:S (1979), 1093–1096, translated inSoviet Mathematics Doklady 20:1 (1979), 191–194. · Zbl 0414.90086 |

[5] | V. Klee andG. L. Minty, How good is the simplex algorithm? inInequalities III, (ed. O. Shisha) Academic Press, New York, 1972, 159–179. |

[6] | O. Veblen andJ. W. Young,Projective Geometry, 1–2, Blaisdell, New York, (1938). |

[7] | R. J. Walker,Algebraic Curves, Princeton University Press (1950). · Zbl 0039.37701 |

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