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.

In the presence of certain non-minimal couplings between a scalar field and Gauss-Bonnet curvature invariant, Kerr black holes can scalarize, as long they are spinning fast enough. This provides distinctive violation hypothesis, occurring only for some high spin range. this paper we assess if strong magnetic fields, that may exist in vicinity astrophysical holes, could facilitate effect, by bri...

We investigate one stage stochastic multiobjective optimization problems where the objectives are the expected values of random functions. Assuming that the closed form of the expected values is difficult to obtain, we apply the well known Sample Average Approximation (SAA) method to solve it. We propose a smoothing infinity norm scalarization approach to solve the SAA problem and analyse the c...

*Correspondence: [email protected] 1School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China 2Research Center of Applied Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China Full list of author information is available at the end of the article Abstract In this paper, we introduce the notion of a quasi-contraction restricted with a ...

Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring often comes at the cost accuracy. We provide formal tools this work for reconciling fundamental tension algorithm fairness. Specifically, we put use concept Pareto optimality from multiobjective optimization seek fairness-accuracy front a neural network classifier. demonstr...

We consider vector optimization problems on Banach spaces without convexity assumptions. Under the assumption that the objective function is locally Lipschitz we derive Lagrangian necessary conditions on the basis of Mordukhovich subdifferential and the approximate subdifferential by Ioffe using a non-convex scalarization scheme. Finally, we apply the results for deriving necessary conditions f...