Nonlinear Programming Analysis And Methods

By Marina Fruehauf. 2016

366 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/66279ac408c723fb6de143d09a10330d7010cb12

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Citations in the corpus

Relatively Smooth Convex Optimization by First-Order Methods, and Applications (2016, unknown): “Since g(·) is a convex function, we know from conjugacy theory [5] that g(y) = supt∈D∗{ty − g∗(t)}.”
Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism (2017, unknown): “constraints in Table I [31], [32].”
Challenges of Big Data Analysis (2013, unknown): “Themost widely used convex optimization algorithm is gradient descent [96], which finds a solution sequence converging to the optimum β̂ by calculating the gradient of the objective function at each point.”
Privacy-Preserving Collaborative Deep Learning with Irregular Participants. (2018, methodology): “The typical algorithms used to solve the problem are different types of gradient descent methods [29].”
Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment (2019, methodology): “Algorithms used for solving this optimization problem are variants of the gradient descent algorithm [5].”
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment (2019, methodology): “Algorithms used for solving this optimization problem are variants of the gradient descent algorithm [24].”
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss (2015, methodology): “(8) Algorithm 1: Inexact damped Newton method input: initial point w0 and specification of a nonnegative sequence {ǫk}. repeat for k = 0, 1, 2, . . . 1.”