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A New Advanced Class of Convex Functions with Related Results
Axioms, 2023 It is the purpose of this paper to propose a novel class of convex functions associated with strong η-convexity. A relationship between the newly defined function and an earlier generalized class of convex functions is hereby established.
Muhammad Adil Khan +3 more
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Non-convex Optimization for Machine Learning [PDF]
Found. Trends Mach. Learn., 2017 A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to capture the learning and prediction problems accurately, structural constraints such as sparsity or low rank are ...
Prateek Jain, Purushottam Kar
semanticscholar +1 more source
First order derivatives new h.hadamard type ınequalities for harmonically h convex functions
Cumhuriyet Science Journal, 2020 In this study, we derived a new integral identity for differentiable functions. However, we get new inequalities which is well known as Hermite-Hadamard (H-H) type by using the integral identity, which unifies the class of new and known harmonically ...
Merve Kule, Mehmet Eyüp Kiriş
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Convex Defining Functions for Convex Domains [PDF]
Journal of Geometric Analysis, 2010 21 ...
Jeffery D. McNeal, A. K. Herbig
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Stochastic model-based minimization of weakly convex functions [PDF]
SIAM Journal on Optimization, 2018 We consider an algorithm that successively samples and minimizes stochastic models of the objective function. We show that under weak-convexity and Lipschitz conditions, the algorithm drives the expected norm of the gradient of the Moreau envelope to ...
Damek Davis, D. Drusvyatskiy
semanticscholar +1 more source
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations [PDF]
IEEE Transactions on Information Theory, 2013 We consider derivative-free algorithms for stochastic and nonstochastic convex optimization problems that use only function values rather than gradients.
John C. Duchi +3 more
semanticscholar +1 more source
On φ-convexity of convex functions
Linear Algebra and its Applications, 1998 The authors construct a non-trivial set \(\Phi\) of extended-real valued functions on \(R^n\) containing all affine functions, such that an extended-real valued function defined on \(R^n\) is convex if and only if it is \(\Phi\)-convex, i.e., it is the pointwise supremum of some subset of \(\Phi\). They also prove a new sandwich theorem.
Ivan Singer +1 more
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Conditionally approximately convex functions
Demonstratio Mathematica, 2016 Let X be a real normed space, V be a subset of X and α: [0, ∞) → [0, ∞] be a nondecreasing function. We say that a function f : V → [−∞, ∞] is conditionally α-convex if for each convex combination ∑i=0ntivi$\sum\nolimits_{i = 0}^n {t_i v_i }$ of ...
Najdecki Adam, Tabor Józef
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An application of the generalized Bessel function [PDF]
Mathematica Bohemica, 2017 We introduce and study some new subclasses of starlike, convex and close-to-convex functions defined by the generalized Bessel operator. Inclusion relations are established and integral operator in these subclasses is discussed.
Hanan Darwish +2 more
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Projections Onto Convex Sets (POCS) Based Optimization by Lifting [PDF]
, 2013 Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented.
Bozkurt, A. +7 more
core +2 more sources