In this paper we suggest an approach for solving a multiobjective stochastic linear programming problem with normal multivariate distributions. Our is combination between method and nonconvex technique. The first transformed into deterministic introducing the expected value criterion utility function that represents decision makers preferences. obtained reduced to mono-objective quadratic using...

M-type smoothing splines are a broad class of spline estimators that include the popular least-squares but also less susceptible to outlying observations and model misspecification. However, available asymptotic theory only covers based on smooth objective functions consequently leaves out frequently used resistant such as quantile Huber-type splines. We provide general treatment in this paper ...

In a multi-objective optimization process, several goals are traditionally combined into single fitness function. such cases, the choice of objective function is critical, as it should accurately represent desired goals. Here, we introduce new class functions with non-linearity and switching behavior, also provide method for engineering. Notably, proposed versatile forms growth during optimizat...

John Searle has argued that functions owe their existence to the value that we put into life and survival. In this paper, I will provide a critique of Searle’s argument concerning the ontology of functions. I rely on a standard analysis of functional predicates as relating not only a biological entity (e.g., the heart), an activity that constitutes the function of this entity (e.g., pumping blo...

Many neural networks can be derived as optimization dynamics for suitable objective functions. We show that such networks can be designed by repeated transformations of one objective into another with the same xpoints. We exhibit a collection of algebraic transformations which reduce network cost and increase the set of objective functions that are neurally implementable. The transformations in...

Sequential optimality conditions provide adequate theoretical tools to justify stopping criteria for nonlinear programming solvers. Here, nonsmooth approximate gradient projection and complementary approximate Karush-Kuhn-Tucker conditions are presented. These sequential optimality conditions are satisfied by local minimizers of optimization problems independently of the fulfillment of constrai...