‎in this paper, using the idea of convexificators, we study boundedness and nonemptiness of lagrange multipliers satisfying the first order necessary conditions. we consider a class of nons- mooth fractional programming problems with equality, inequality constraints and an arbitrary set constraint. within this context, define generalized mangasarian-fromovitz constraint qualification and show t...

Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions solved backtracking tree search augmented logical inference. this paper, we show how quantum algorithms can accelerate at both the levels of inference search. Leveraging existing algorithms,...

Abstract Handling stress constraints is an important topic in topology optimization. In this paper, we introduce interpretation of stresses as optimization variables, leading to augmented Lagrangian formulation. This formulation takes two sets i.e., auxiliary variable per element, addition a density conventional density-based approaches. The related the actual (i.e., computed by its definition)...

In this paper, a general error analysis is provided for finite-element discretizations of partial differential equations in a saddle-point form with divergence constraint. In particular, this extends upon the work of [J. H. Adler and P. S. Vassilevski, Springer Proc. Math. Statist. 45, Springer, New York, 2013, pp. 1–19], giving a general error estimate for finite-element problems augmented wit...

A resurgence of interest in the role of banks in the transmission of monetary policy has resulted in a spate of theoretical and empirical studies. These studies have established that, under certain conditions, the traditional transmission mechanism for monetary policy ("the money view") may be augmented through changes in the supply of bank loans ("the lending view"). Because both the money vie...

The constant rank constraint qualification, introduced by Janin in 1984 for nonlinear programming, has been extensively used sensitivity analysis, global convergence of first- and second-order algorithms, computing the directional derivative value function. In this paper we discuss naive extensions rank-type qualifications to cone programming semidefinite which are based on Approximate-Karush–K...

This paper presents the application of multiple objective linear programming for power generation expansion planning on Milos island. The model considers those economic and environmental objectives which typically conflict (cost minimization vs. CO2 emission reduction maximization) subject to a number of constraints. Due to uncertainties in future power demand, the latter is handled as a fuzzy ...

Electrical impedance tomography (EIT) attempts to reconstruct the internal impedance distribution in a medium from electrical measurements at electrodes on the medium surface. One key difficulty with EIT measurements is due to the position uncertainty of the electrodes, especially for medical applications, in which the body surface moves during breathing and posture change. In this paper, we de...

First-order methods have been popularly used for solving large-scale problems. However, many existing works only consider unconstrained problems or those with simple constraint. In this paper, we develop two first-order methods for constrained convex programs, for which the constraint set is represented by affine equations and smooth nonlinear inequalities. Both methods are based on the classic...

In the encoding of many real-world problems to propositional satisfiability, the cardinality constraint is a recurrent constraint that needs to be managed effectively. Several efficient encodings have been proposed while missing that such a constraint can be involved in a more general propositional formulation. To avoid combinatorial explosion, Tseitin principle usually used to translate such g...