We present new necessary optimality conditions for εe–Pareto optimal solutions of constrained vector optimization problems with empty interior ordering cones. We use the dual-space approach based on advanced tools of variational analysis and generalized differentiation. It allows us not implement any scalarization technique while be able to establish necessary results for nonconvex and nonsolid...

Abstract. Construction of complex array operations by composition of more basic ones allows for abstract and concise specifications of algorithms. Unfortunately, näıve compilation of such specifications leads to creation of many temporary arrays at runtime and, consequently, to poor performance characteristics. This paper elaborates on a new compiler optimization, named withloop-scalarization, ...

A decade ago, it was shown that a wide class of scalar-tensor theories can pass very restrictive weak field tests of gravity and yet exhibit non-perturbative strong field deviations away from General Relativity. This phenomenon was called ‘Spontaneous Scalarization’ and causes the (Einstein frame) scalar field inside a neutron star to rapidly become inhomogeneous once the star’s mass increases ...

This paper contains the equivalence between tvs-G cone metric and G-metric using a scalarization function $\zeta_p$, defined over locally convex Hausdorff topological vector space. ensures that most studies on existence uniqueness of fixed-point theorems space spaces are equivalent. We prove vector-valued version scalar-valued those spaces. Moreover, we present if real Banach is considered inst...

A Multi-objective optimization procedure has been developed to determine some kinetic parameters of free radical polymerization of vinyl acetate based on genetic algorithm. For this purpose, mathematical modeling of free radical polymerization of vinyl acetate is carried out first and then selected kinetic parameters are optimized by minimizing objective functions defined from comparing exp...

Overparameterization and overfitting are common concerns when designing training deep neural networks, that often counteracted by pruning regularization strategies. However, these strategies remain secondary to most learning approaches suffer from time computational intensive procedures. We suggest a multiobjective perspective on the of networks treating its prediction accuracy network complexi...

Scalar-tensor theories of gravity are known to allow significant deviations from general relativity through various astrophysical phenomena. In this paper, we formulate a scalar-connection by setting up scalars and connection configurations instead metric. Since the matter sector is not straightforward conceive without metric, invoke cosmological fluids in terms their one-form velocity volume e...

Abstract We propose a new formulation of f ( R ) gravity, dubbed scalarized in which the Legendre transform is included as dynamical term. This leads to theory with second-order field equations that describes general relativity self-interacting scalar field, without requiring introduction conformal frames. demonstrate quadratic version gravity reduces massive and we explore its implications for...

In this paper, proper optimality concepts in vector optimization with variable ordering structures are introduced for the first time and characterization results via scalarizations are given. New type of scalarizing functionals are presented and their properties are discussed. The scalarization approach suggested in the paper does not require convexity and boundedness conditions.