Jun 22, 2022 · This framework is widely used in machine learning problems, including meta-learning, data hyper-cleaning, and matrix completion with denoising.
In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function.
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4 days ago · The authors utilize a quantum-inspired evolutionary algorithm (QEA) coupled with a Frank–Wolfe algorithm. Recently, Fan et al. (2021) examine a ...
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in large scale optimization for ...
The Frank–Wolfe algorithm is an iterative first-order optimization algorithm for constrained convex optimization. Also known as the conditional gradient method.
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Jul 4, 2024 · Generalized stochastic Frank–Wolfe algorithm with stochastic “substitute” gradient for structured convex optimization. Mathematical Pro ...
In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function ...
Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a ...
Apr 3, 2022 · This work puts forth the Stochastic Bi-level Frank-Wolfe (SBFW) algorithm, which is the first projection-free algorithm for bi-level problems.
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Frank Wolfe Method: Find a feasible direction by minimizing linear approximation of objective sk ← arg min s∈X∗ g h∇f(xk), si xk+1 ← (1 − γk)xk + γksk.
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