Small errors in random zeroth-order optimization are imaginary

Cites Work

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First-order methods of smooth convex optimization with inexact oracle
Do you trust derivatives or differences?
Optimal order of accuracy of search algorithms in stochastic optimization
The complex step approximation to the Fréchet derivative of a matrix function
On the accuracy of the complex-step-finite-difference method
Practical mathematical optimization. Basic optimization theory and gradient-based algorithms
A new one-point residual-feedback oracle for black-box learning and control
A theoretical and empirical comparison of gradient approximations in derivative-free optimization
A one-bit, comparison-based gradient estimator
Improved exploitation of higher order smoothness in derivative-free optimization
Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case
Random gradient-free minimization of convex functions
Complex-step derivative approximation in noisy environment
Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations
Zeroth-order nonconvex stochastic optimization: handling constraints, high dimensionality, and saddle points
Numerical computing with IEEE floating point arithmetic. Incl. one theorem, one rule of thumb, and one hundred and one exercises
Optimization of convex functions with random pursuit
Introduction to Smooth Manifolds
Beautiful differentiation
Julia: A Fresh Approach to Numerical Computing
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations
Using Multicomplex Variables for Automatic Computation of High-Order Derivatives
When is a Function that Satisfies the Cauchy-Riemann Equations Analytic?
Smooth Optimization with Approximate Gradient
Evaluating Derivatives
Introduction to Derivative-Free Optimization
Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
Using Complex Variables to Estimate Derivatives of Real Functions
Derivative-Free and Blackbox Optimization
Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
Finite Difference Gradient Approximation: To Randomize or Not?
Five stages of accepting constructive mathematics
Derivative-free optimization methods
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
The complex-step derivative approximation
Numerical Differentiation of Analytic Functions
A Simplex Method for Function Minimization
Stochastic Estimation of the Maximum of a Regression Function
On the numerical performance of finite-difference-based methods for derivative-free optimization
Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization

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