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[BOK][B] Modern nonconvex nondifferentiable optimization
Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …
economics. In these applied domains, optimization concepts and methods have often been …
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
Classical stochastic gradient methods are well suited for minimizing expected-value
objective functions. However, they do not apply to the minimization of a nonlinear function …
objective functions. However, they do not apply to the minimization of a nonlinear function …
A single timescale stochastic approximation method for nested stochastic optimization
We study constrained nested stochastic optimization problems in which the objective
function is a composition of two smooth functions whose exact values and derivatives are …
function is a composition of two smooth functions whose exact values and derivatives are …
Accelerating stochastic composition optimization
We consider the stochastic nested composition optimization problem where the objective is
a composition of two expected-value functions. We propose a new stochastic first-order …
a composition of two expected-value functions. We propose a new stochastic first-order …
Finite-sum composition optimization via variance reduced gradient descent
The stochastic composition optimization proposed recently by Wang et al.[2014] minimizes
the objective with the composite expectation form: $\min_x (\mathbbE_iF_i∘\mathbbE_j …
the objective with the composite expectation form: $\min_x (\mathbbE_iF_i∘\mathbbE_j …
Stochastic multilevel composition optimization algorithms with level-independent convergence rates
In this paper, we study smooth stochastic multilevel composition optimization problems,
where the objective function is a nested composition of T functions. We assume access to …
where the objective function is a nested composition of T functions. We assume access to …
A stochastic subgradient method for distributionally robust non-convex and non-smooth learning
We consider a distributionally robust formulation of stochastic optimization problems arising
in statistical learning, where robustness is with respect to ambiguity in the underlying data …
in statistical learning, where robustness is with respect to ambiguity in the underlying data …
Multilevel stochastic gradient methods for nested composition optimization
Stochastic gradient methods are scalable for solving large-scale optimization problems that
involve empirical expectations of loss functions. Existing results mainly apply to optimization …
involve empirical expectations of loss functions. Existing results mainly apply to optimization …