scholarly journals A Projected Forward-Backward Algorithm for Constrained Minimization with Applications to Image Inpainting

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 890
Author(s):  
Suthep Suantai ◽  
Kunrada Kankam ◽  
Prasit Cholamjiak
Keyword(s):  
Image Processing ◽  
Minimization Problem ◽  
Convex Functions ◽  
Lower Semicontinuous ◽  
Image Inpainting ◽  
Constrained Minimization ◽  
Convex Minimization Problem ◽  
Mild Conditions ◽  
Constrained Minimization Problem ◽  
Weak Convergence Theorem

In this research, we study the convex minimization problem in the form of the sum of two proper, lower-semicontinuous, and convex functions. We introduce a new projected forward-backward algorithm using linesearch and inertial techniques. We then establish a weak convergence theorem under mild conditions. It is known that image processing such as inpainting problems can be modeled as the constrained minimization problem of the sum of convex functions. In this connection, we aim to apply the suggested method for solving image inpainting. We also give some comparisons to other methods in the literature. It is shown that the proposed algorithm outperforms others in terms of iterations. Finally, we give an analysis on parameters that are assumed in our hypothesis.

scholarly journals A Relax Inexact Accelerated Proximal Gradient Method for the Constrained Minimization Problem of Maximum Eigenvalue Functions

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Wei Wang ◽  
Shanghua Li ◽  
Jingjing Gao
Keyword(s):  
Minimization Problem ◽  
Feasible Solution ◽  
Lower Semicontinuous ◽  
Constrained Minimization ◽  
Maximum Eigenvalue ◽  
Proximal Gradient Method ◽  
Smooth Approximation ◽  
Computational Advantage ◽  
Global Iteration ◽  
Constrained Minimization Problem

For constrained minimization problem of maximum eigenvalue functions, since the objective function is nonsmooth, we can use the approximate inexact accelerated proximal gradient (AIAPG) method (Wang et al., 2013) to solve its smooth approximation minimization problem. When we take the functiong(X)=δΩ(X)  (Ω∶={X∈Sn:F(X)=b,X⪰0})in the problemmin{λmax(X)+g(X):X∈Sn}, whereλmax(X)is the maximum eigenvalue function,g(X)is a proper lower semicontinuous convex function (possibly nonsmooth) andδΩ(X)denotes the indicator function. But the approximate minimizer generated by AIAPG method must be contained inΩotherwise the method will be invalid. In this paper, we will consider the case where the approximate minimizer cannot be guaranteed inΩ. Thus we will propose two different strategies, respectively, constructing the feasible solution and designing a new method named relax inexact accelerated proximal gradient (RIAPG) method. It is worth mentioning that one advantage when compared to the former is that the latter strategy can overcome the drawback. The drawback is that the required conditions are too strict. Furthermore, the RIAPG method inherits the global iteration complexity and attractive computational advantage of AIAPG method.

Finite Strain Elastostatics With Stiffening Materials: A Constrained Minimization Model

1997 ◽  
Vol 64 (2) ◽  
pp. 440-442 ◽  
Author(s):  
S. J. Hollister ◽  
J. E. Taylor ◽  
P. D. Washabaugh
Keyword(s):  
Boundary Value Problem ◽  
Minimization Problem ◽  
Finite Strain ◽  
Necessary Conditions ◽  
Piecewise Linear ◽  
Process Parameter ◽  
Constrained Minimization ◽  
Problem Statement ◽  
Stress Strain ◽  
Constrained Minimization Problem

Finite strain elastostatics is expressed for general anisotropic, piecewise linear stiffening materials, in the form of a constrained minimization problem. The corresponding boundary value problem statement is identified with the associated necessary conditions. Total strain is represented as a superposition of variationally independent constituent fields. Net stress-strain properties in the model are implicit in terms of the parameters that define the constituents. The model accommodates specification of load fields as functions of a process parameter.

scholarly journals Weak and Strong Superiorization: Between Feasibility-Seeking and Minimization

2015 ◽  
Vol 23 (3) ◽  
pp. 41-54 ◽  
Author(s):  
Yair Censor
Keyword(s):  
Objective Function ◽  
Minimization Problem ◽  
General Principle ◽  
Objective Function Value ◽  
Constrained Minimization ◽  
Feasible Point ◽  
Research Directions ◽  
Constrained Minimization Problem

Abstract We review the superiorization methodology, which can be thought of, in some cases, as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full edged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to an objective function value) to one returned by a feasibility-seeking only algorithm. We distinguish between two research directions in the superiorization methodology that nourish from the same general principle: Weak superiorization and strong superiorization and clarify their nature.

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