Two gradient properties of explicitly convex functions

Random Gradient-Free Minimization of Convex Functions

In this paper, we prove the complexity bounds for methods of Convex Optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors. It appears that such methods usually need at most n times more iterations than the standard gradient methods, where n is the dimension of the space of variables. This conclusion is...

Some Properties of Certain Subclasses of Close-to-Convex and Quasi-convex Functions with Respect to 2k-Symmetric Conjugate Points

PROPERTIES OF CERTAIN p-VALENTLY CONVEX FUNCTIONS

for some α (0 α< 1). We denote by p(α) the subclass of p consisting of functions which are p-valently convex of order α in U. In particular, we denote 1(0)= . A function f(z)∈ 1 is said to be uniformly convex inU if f(z) is in the class and has the property that the image arc f(γ) is convex for every circular arc γ contained in U with center at t ∈ U. We also denote by the subclass of 1 consist...

Smoothness Properties of Generalized Convex Functions

We present a concise and elementary proof of a theorem of Karlin and Studden concerning the smoothness properties of functions belonging to a generalized convexity cone. In [1, Chapter XI], Karlin and Studden showed that a function which is convex with respect to an extended complete Tchebycheff system has a continuous derivative of order n — 1, a fact which is of considerable importance in the...

Singular values of convex functions of matrices

‎Let $A_{i},B_{i},X_{i},i=1,dots,m,$ be $n$-by-$n$ matrices such that $‎sum_{i=1}^{m}leftvert A_{i}rightvert ^{2}$ and $‎sum_{i=1}^{m}leftvert B_{i}rightvert ^{2}$  are nonzero matrices and each $X_{i}$ is‎ ‎positive semidefinite‎. ‎It is shown that if $f$ is a nonnegative increasing ‎convex function on $left[ 0,infty right) $ satisfying $fleft( 0right)‎ ‎=0 $‎, ‎then  $$‎2s_{j}left( fleft( fra...