An Out Space Accelerating Algorithm for Generalized Affine Multiplicative Programs Problem

Abstract

This paper presents an out space branch-and-bound algorithm for solving generalized affine multiplicative programs problem. Firstly, by introducing new variables and constraints, we transform the original problem into an equivalent nonconvex programs problem. Secondly, by utilizing new linear relaxation technique, we establish the linear relaxation programs problem of the equivalent problem. Thirdly, based on the out space partition and the linear relaxation programs problem, we construct an out space branch-and-bound algorithm. Fourthly, to improve the computational efficiency of the algorithm, an out space reduction operation is employed as an accelerating device for deleting a large part of the investigated out space region. Finally, the global convergence of the algorithm is proved, and numerical results demonstrate the feasibility and effectiveness of the proposed algorithm.

Dynamic Multiscale Feature Fusion Method for Underwater Target Recognition

“…The method uses similarity constraints to capture the relationship between available and privileged information and ranking constraints to capture the dependencies between multiple labels. To improve the convergence speed of the algorithm, Cai et al [14] designed an effective outer space acceleration algorithm (GAMP). Experimental results show that the algorithm has higher computational efficiency.…”

Underwater Distortion Target Recognition Network (UDTRNet) via Enhanced Image Features

“…e method uses the visual and semantic representation of the image to predict the class of the image. To improve the convergence speed of the algorithm, Cai et al [18] designed an effective outer space acceleration algorithm. Sun and Cai [19] proposed a multi-AUV target recognition method based on GANmeta learning.…”