This paper proposes a novel decentralized stochastic first-order optimization algorithm, which does not require second-order Hessian or Jacobian matrices, for the setting where the lower-level loss function is nonconvex but satisfies the Polyak–Łojasiewicz (PL) condition.
This work proposes a novel single-loop algorithm based on a constrained reformulation in which lower-level stationarity is imposed as a constraint, and constructs a regularized Lagrangian by introducing a quadratic regularizer and restricting the dual variable to a bounded domain.
A randomized two-point direct-search algorithm for nonconvex time-varying optimization and derive iteration-complexity bounds under both constant and diminishing probing ratios, which recover the complexity of existing zeroth-order methods in the time-invariant setting while extending direct- search methods beyond stat...
This paper proposes a general line-search Newton framework for unconstrained optimization that avoids repeated Hessian regularization by exploiting the Newton direction only when it is well-defined and suitable and provides the first Newton-type algorithm together with a comprehensive convergence analysis for this impo...
In this paper, we introduce a new optimization algorithm that is well suited to solve parameter estimation problems that arise when inferring heterogeneous population dynamics. In these estimation problems, parameter estimation is complicated by the presence of two types of constraints: inequality constraints (e.g., no...
For solving nonconvex equality-constrained optimization problems, a recent Gradient-Eigenstep Algorithm by Goyens et al.~is an iteration-efficient approach, based on minimizing Fletcher's augmented Lagrangian function, for finding an approximate second-order stationary point from an arbitrary starting point. In this pa...
F. Curtis, Ling-Jun Guo, Daniel P. Robinson· 0 citations
This work introduces a proximal stochastic subgradient method for minimizing the sum of an expected cost, whose integrand is potentially nonsmooth and nonconvex, and a lower semicontinuous, prox-bounded function. We target a broad class of integrands obeying a nonsmooth, localized variant of the descent lemma in the de...
Felipe Atenas, Alejandro Jofré, Pedro Pérez-Aros et al.· 0 citations
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