Quasi Semi and Pseudo Semi (p,E)-Convexity in Non-Linear Optimization Programming
نویسندگان
چکیده
The class of quasi semi -convex functions and pseudo are presented in this paper by combining the with functions, respectively. Various non-trivial examples introduced to illustrate new show their relationships recently literature. Different general properties characteristics established. In addition, some optimality generalized non-linear optimization problems discussed. problems, we used, as objective function, (respectively, strictly functions), constraint set is set.
منابع مشابه
Quasi-Gap and Gap Functions for Non-Smooth Multi-Objective Semi-Infinite Optimization Problems
In this paper, we introduce and study some new single-valued gap functions for non-differentiable semi-infinite multiobjective optimization problems with locally Lipschitz data. Since one of the fundamental properties of gap function for optimization problems is its abilities in characterizing the solutions of the problem in question, then the essential properties of the newly introduced ...
متن کاملDropping Convexity for Faster Semi-definite Optimization
We study the minimization of a convex function f(X) over the set of n × n positive semi-definite matrices, but when the problem is recast as minU g(U) := f(UU >), with U ∈ Rn×r and r ≤ n. We study the performance of gradient descent on g—which we refer to as Factored Gradient Descent (Fgd)—under standard assumptions on the original function f . We provide a rule for selecting the step size and,...
متن کاملSome new results on semi fully fuzzy linear programming problems
There are two interesting methods, in the literature, for solving fuzzy linear programming problems in which the elements of coefficient matrix of the constraints are represented by real numbers and rest of the parameters are represented by symmetric trapezoidal fuzzy numbers. The first method, named as fuzzy primal simplex method, assumes an initial primal basic feasible solution is at hand. T...
متن کاملCSC 2411 - Linear Programming and Combinatorial Optimization ∗ Lecture 9 : Semi - Definite Programming Combinatorial Optimization
This lecture consists of two main parts. In the first one, we revisit Semi-Definite Programming (SDP). We show its equivalence to Vector Programming, we prove it has efficient membership and separation oracles and finally state a theorem that shows why Ellipsoid can be used to acquire an approximate solution of a semi-definite program. In the second part, we make a first approach to Combinatori...
متن کاملSolving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Ibn Al-Haitham Journal For Pure And Applied Science
سال: 2023
ISSN: ['2521-3407', '1609-4042']
DOI: https://doi.org/10.30526/36.1.2928