Pages that link to "Item:Q1679617"

The following pages link to On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions (Q1679617):

Displaying 31 items.

• Exact worst-case convergence rates of the proximal gradient method for composite convex minimization (Q1670100) (← links)
• Analysis of biased stochastic gradient descent using sequential semidefinite programs (Q2020610) (← links)
• Analysis of optimization algorithms via sum-of-squares (Q2046552) (← links)
• Optimal step length for the Newton method: case of self-concordant functions (Q2067259) (← links)
• A frequency-domain analysis of inexact gradient methods (Q2149575) (← links)
• Efficient first-order methods for convex minimization: a constructive approach (Q2205976) (← links)
• New stepsizes for the gradient method (Q2228379) (← links)
• A note on the optimal convergence rate of descent methods with fixed step sizes for smooth strongly convex functions (Q2671453) (← links)
• The exact worst-case convergence rate of the gradient method with fixed step lengths for \(L\)-smooth functions (Q2673524) (← links)
• Smoothing and worst-case complexity for direct-search methods in nonsmooth optimization (Q2841063) (← links)
• Tight Sublinear Convergence Rate of the Proximal Point Algorithm for Maximal Monotone Inclusion Problems (Q3300773) (← links)
• Generalizing the Optimized Gradient Method for Smooth Convex Minimization (Q4571883) (← links)
• Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients (Q4594856) (← links)
• Primal–dual accelerated gradient methods with small-dimensional relaxation oracle (Q5085262) (← links)
• Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Estimation (Q5116548) (← links)
• Analysis of the gradient method with an Armijo–Wolfe line search on a class of non-smooth convex functions (Q5210738) (← links)
• Exact Worst-Case Performance of First-Order Methods for Composite Convex Optimization (Q5275297) (← links)
• Worst case complexity of direct search under convexity (Q5962720) (← links)
• An optimal gradient method for smooth strongly convex minimization (Q6038652) (← links)
• Conditions for linear convergence of the gradient method for non-convex optimization (Q6097482) (← links)
• Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods (Q6120850) (← 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)
• Adaptive Catalyst for Smooth Convex Optimization (Q6329866) (← 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)
• On the rate of convergence of the difference-of-convex algorithm (DCA) (Q6596346) (← links)
• Interpolation conditions for linear operators and applications to performance estimation problems (Q6601207) (← links)
• Convergence rate analysis of the gradient descent–ascent method for convex–concave saddle-point problems (Q6644990) (← links)
• Several kinds of acceleration techniques for unconstrained optimization first-order algorithms (Q6665360) (← links)