A selection principle with applications in convex analysis and optimization

Constraint aggregation principle in convex optimization

A general constraint aggregation technique is proposed for convex optimization problems. At each iteration a set of convex inequalities and linear equations is replaced by a single inequality formed as a linear combination of the original constraints. After solving the simplified subproblem, new aggregation coefficients are calculated and the iteration continues. This general aggregation princi...

a contrastive analysis of concord and head parameter in english and azerbaijani

این پایان نامه به بررسی و مقایسه دو موضوع مطابقه میان فعل و فاعل (از نظر شخص و مشار) و هسته عبارت در دو زبان انگلیسی و آذربایجانی می پردازد. اول رابطه دستوری مطابقه مورد بررسی قرار می گیرد. مطابقه به این معناست که فعل مفرد به همراه فاعل مفرد و فعل جمع به همراه فاعل جمع می آید. در انگلیسی تمام افعال، بجز فعل بودن (to be) از نظر شمار با فاعلشان فقط در سوم شخص مفرد و در زمان حال مطابقت نشان میدهند...

cohesion and cohesive devices in a contrastive analysis between ge and esp texts

the present study was an attempt to conduct a contrastive analysis between general english (ge) and english for specific purposes (esp) texts in terms of cohesion and cohesive devices. to this end, thirty texts from different esp and ge textbooks were randomly selected. then they were analyzed manually to find the frequency of cohesive devices. cohesive devices include reference, substitution, ...

Convex Analysis and Optimization

A Semidefinite Optimization Approach to Quadratic Fractional Optimization with a Strictly Convex Quadratic Constraint

In this paper we consider a fractional optimization problem that minimizes the ratio of two quadratic functions subject to a strictly convex quadratic constraint. First using the extension of Charnes-Cooper transformation, an equivalent homogenized quadratic reformulation of the problem is given. Then we show that under certain assumptions, it can be solved to global optimality using semidefini...