Semidefinite programming for permutation codes
نویسندگان
چکیده
We initiate study of the Terwilliger algebra and related semidefinite programming techniques for the conjugacy scheme of the symmetric group Sym(n). In particular, we compute orbits of ordered pairs on Sym(n) acted upon by conjugation and inversion, explore a block di-agonalization of the associated algebra, and obtain improved upper bounds on the size M (n, d) of permutation codes of lengths up to 7. For instance, these techniques detect the nonexistence of the projective plane of order six via M (6, 5) < 30 and yield a new best bound M (7, 4) ≤ 535 for a challenging open case. Each of these represents an improvement on earlier Delsarte linear programming results.
منابع مشابه
A Recurrent Neural Network Model for Solving Linear Semidefinite Programming
In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...
متن کاملReduction of symmetric semidefinite programs using the regular *-representation
Abstract. We consider semidefinite programming problems on which a permutation group is acting. We describe a general technique to reduce the size of such problems, exploiting the symmetry. The technique is based on a low-order matrix ∗-representation of the commutant (centralizer ring) of the matrix algebra generated by the permutation matrices. We apply it to extending a method of de Klerk et...
متن کاملBounds for projective codes from semidefinite programming
We apply the semidefinite programming method to derive bounds for projective codes over a finite field.
متن کاملBounds for Codes by Semidefinite Programming
Delsarte’s method and its extensions allow to consider the upper bound problem for codes in 2-point-homogeneous spaces as a linear programming problem with perhaps infinitely many variables, which are the distance distribution. We show that using as variables power sums of distances this problem can be considered as a finite semidefinite programming problem. This method allows to improve some l...
متن کاملA path following interior-point algorithm for semidefinite optimization problem based on new kernel function
In this paper, we deal to obtain some new complexity results for solving semidefinite optimization (SDO) problem by interior-point methods (IPMs). We define a new proximity function for the SDO by a new kernel function. Furthermore we formulate an algorithm for a primal dual interior-point method (IPM) for the SDO by using the proximity function and give its complexity analysis, and then we sho...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Discrete Mathematics
دوره 326 شماره
صفحات -
تاریخ انتشار 2014