Table of Contents
9 relations: Convex optimization, Convex set, Linear-fractional programming, Loss function, Mathematical optimization, Nonlinear programming, Pseudoconvex function, Quasiconvex function, Real-valued function.
Convex optimization
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets).
See Fractional programming and Convex optimization
Convex set
In geometry, a subset of a Euclidean space, or more generally an affine space over the reals, is convex if, given any two points in the subset, the subset contains the whole line segment that joins them.
See Fractional programming and Convex set
Linear-fractional programming
In mathematical optimization, linear-fractional programming (LFP) is a generalization of linear programming (LP). Fractional programming and linear-fractional programming are optimization algorithms and methods.
See Fractional programming and Linear-fractional programming
Loss function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
See Fractional programming and Loss function
Mathematical optimization
Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives.
See Fractional programming and Mathematical optimization
Nonlinear programming
In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints are not linear equalities or the objective function is not a linear function. Fractional programming and nonlinear programming are optimization algorithms and methods.
See Fractional programming and Nonlinear programming
Pseudoconvex function
In convex analysis and the calculus of variations, both branches of mathematics, a pseudoconvex function is a function that behaves like a convex function with respect to finding its local minima, but need not actually be convex.
See Fractional programming and Pseudoconvex function
Quasiconvex function
In mathematics, a quasiconvex function is a real-valued function defined on an interval or on a convex subset of a real vector space such that the inverse image of any set of the form (-\infty,a) is a convex set.
See Fractional programming and Quasiconvex function
Real-valued function
In mathematics, a real-valued function is a function whose values are real numbers.
See Fractional programming and Real-valued function