Pages that link to "Item:Q2026726"
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The following pages link to Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions (Q2026726):
Displaying 26 items.
- Optimized first-order methods for smooth convex minimization (Q312663) (← links)
- Smooth strongly convex interpolation and exact worst-case performance of first-order methods (Q507324) (← links)
- New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure (Q1659678) (← links)
- Nearly optimal first-order methods for convex optimization under gradient norm measure: an adaptive regularization approach (Q2031939) (← links)
- Analysis of optimization algorithms via sum-of-squares (Q2046552) (← links)
- Efficient first-order methods for convex minimization: a constructive approach (Q2205976) (← links)
- Accelerated proximal point method for maximally monotone operators (Q2235140) (← links)
- Performance of first-order methods for smooth convex minimization: a novel approach (Q2248759) (← links)
- On the oracle complexity of first-order and derivative-free algorithms for smooth nonconvex minimization (Q2902870) (← links)
- Generalizing the Optimized Gradient Method for Smooth Convex Minimization (Q4571883) (← links)
- Potential Function-Based Framework for Minimizing Gradients in Convex and Min-Max Optimization (Q5093649) (← links)
- An acceleration procedure for optimal first-order methods (Q5746718) (← links)
- Generalized Momentum-Based Methods: A Hamiltonian Perspective (Q5857293) (← links)
- An optimal gradient method for smooth strongly convex minimization (Q6038652) (← links)
- Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods (Q6120850) (← links)
- Optimal data splitting in distributed optimization for machine learning (Q6124406) (← links)
- Smooth monotone stochastic variational inequalities and saddle point problems: a survey (Q6160072) (← links)
- An elementary approach to tight worst case complexity analysis of gradient based methods (Q6165581) (← links)
- Principled analyses and design of first-order methods with inexact proximal operators (Q6165584) (← links)
- Conic linear optimization for computer-assisted proofs. Abstracts from the workshop held April 10--16, 2022 (Q6170529) (← links)
- Provably faster gradient descent via long steps (Q6579999) (← links)
- Tight ergodic sublinear convergence rate of the relaxed proximal point algorithm for monotone variational inequalities (Q6596341) (← links)
- Interpolation conditions for linear operators and applications to performance estimation problems (Q6601207) (← links)
- Complementary composite minimization, small gradients in general norms, and applications (Q6634528) (← links)
- PEPIT: computer-assisted worst-case analyses of first-order optimization methods in python (Q6645946) (← links)
- Accelerated minimax algorithms flock together (Q6663115) (← links)
