Solving the Perceptron Problem by deterministic optimization approach based on DC programming and DCA

被引：1

作者：

An, Le Thi Hoai ^{[1
]}

Minh, Le Hoai ^{[1
]}

Tao, Pham Dinh ^{[2
]}

Bouvry, Pascal ^{[3
]}

机构：

[1] Univ Paul Verlaine Metz, UFR MIM, Lab Theoret & Appl Comp Sci, F-57045 Metz, France

[2] Natl Inst Appl Sci Rouen, Lab Modelling Optmizat & Operat Res, F-76131 Mont St Aignan, France

[3] Univ Luxemourg, Comp Sci Res Unit, L-1359 Luxembourg, Luxembourg

来源：

2009 7TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, VOLS 1 AND 2 | 2009年

关键词：

Cryptanalysis; Identification scheme; Perceptron Problem; DC programming; DCA; IDENTIFICATION;

D O I：

10.1109/INDIN.2009.5195807

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Perceptron Problem (PP) appeared for the first time in the Learning Machines and is very useful for zero-knowledge identification schemes in cryptology. The problem is NP-complete and no deterministic algorithm is known to date. In this paper we develop a deterministic method based on DC (Difference of Convex functions) programming and DCA (DC optimization Algorithms), an innovative approach in nonconvex programming framework. We first formulate the PP as a concave minimization programming problem. Then, we show how to apply DC programming and DCA for the resulting problem. Numerical results demonstrate that the proposed algorithm is promising: its is very fast and can efficiently solve the Perceptron Problem with large sizes.

引用

页码：222 / +

页数：2

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