Results 211 to 220 of about 59,683 (249)

The Concave-Convex Procedure

Neural Computation, 2003
The concave-convex procedure (CCCP) is a way to construct discrete-time iterative dynamical systems that are guaranteed to decrease global optimization and energy functions monotonically. This procedure can be applied to almost any optimization problem, and many existing algorithms can be interpreted in terms of it.
Anand Rangarajan, Rangarajan Anand, A L Yuille   +2 more
exaly   +5 more sources

On a decoding algorithm for LDPC codes based on the concave-convex procedure

IEEE International Symposium on Information Theory, 2003. Proceedings., 2003
In this paper, we empirically show the effectiveness of double-loop algorithm based on the concave-convex procedure in decoding linear codes.
T. Shibuya, K. Sakaniwa
exaly   +3 more sources

Sparse functional linear models via calibrated concave-convex procedure

Journal of the Korean Statistical Society, 2023
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Young Joo Lee, Yongho Jeon
openaire   +3 more sources

ConCave-Convex procedure for support vector machines with Huber loss for text classification

Computers and Electrical Engineering
Deepak Gupta, Parashjyoti Borah, Barenya Bikash Hazarika   +2 more
exaly   +3 more sources

Scheduling of steelmaking-continuous casting process with different processing routes using effective surrogate Lagrangian relaxation approach and improved concave–convex procedure

International Journal of Production Research, 2021
This paper studies a steelmaking-continuous casting scheduling problem with different processing routes. We model this problem as a mixed-integer nonlinear programming problem.
Haijuan Cui, Xiaochuan Luo, Yuan Wang
openaire   +2 more sources

A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory

Neural Computation, 2012
The concave-convex procedure (CCCP) is an iterative algorithm that solves d.c. (difference of convex functions) programs as a sequence of convex programs. In machine learning, CCCP is extensively used in many learning algorithms, including sparse support vector machines (SVMs), transductive SVMs, and sparse principal component analysis. Though CCCP is
Bharath K. Sriperumbudur, Gert R. G. Lanckriet   +1 more
openaire   +4 more sources

Iterative Decoding Based on the Concave-Convex Procedure

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2005
New decoding algorithms for binary linear codes based on the concave-convex procedure are presented. Numerical experiments show that the proposed decoding algorithms surpass Belief Propagation (BP) decoding in error performance. Average computational complexity of one of the proposed decoding algorithms is only a few times greater than that of the BP ...
T. Shibuya, K. Harada, R. Tohyama, K. Sakaniwa   +3 more
openaire   +2 more sources

Improved Concave-Convex procedure and its application to analysis for the stability of Hopfield neural network

2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 2010
This paper discusses the Improvement of Concave-Convex procedure, where the objective function in optimization problem can be decomposed into a convex function minus a generalized differential function. While preserving the property of monotonic decreasing for optimization objective function, the convergence conditions of this procedure and the scope ...
Shiwei Ye
exaly   +2 more sources

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure

IEEE Transactions on Neural Networks and Learning Systems, 2019
In kernel methods, the kernels are often required to be positive definitethat restricts the use of many indefinite kernels. To consider those nonpositive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel ...
Chen Gong, Johan Suykens, Fanghui Liu
exaly   +2 more sources