Convex optimization using quantum oracles

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Convex Optimization with Nonconvex Oracles

In machine learning and optimization, one often wants to minimize a convex objective function F but can only evaluate a noisy approximation F̂ to it. Even though F is convex, the noise may render F̂ nonconvex, making the task of minimizing F intractable in general. As a consequence, several works in theoretical computer science, machine learning and optimization have focused on coming up with pol...

متن کامل

Efficient Convex Optimization with Membership Oracles

We consider the problem of minimizing a convex function over a convex set given access only to an evaluation oracle for the function and a membership oracle for the set. We give a simple algorithm which solves this problem with Õ(n) oracle calls and Õ(n) additional arithmetic operations. Using this result, we obtain more efficient reductions among the five basic oracles for convex sets and func...

متن کامل

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles

Algorithms for bandit convex optimization and online learning often rely on constructing noisy gradient estimates, which are then used in appropriately adjusted first-order algorithms, replacing actual gradients. Depending on the properties of the function to be optimized and the nature of “noise” in the bandit feedback, the bias and variance of gradient estimates exhibit various tradeoffs. In ...

متن کامل

Level bundle methods for constrained convex optimization with various oracles

We propose restricted memory level bundle methods for minimizing constrained convex nonsmooth optimization problems whose objective and constraint functions are known through oracles (black-boxes) that might provide inexact information. Our approach is general and covers many instances of inexact oracles, such as upper, lower and on-demand accuracy oracles. We show that the proposed level bundl...

متن کامل

Lower Bounds for Convex Optimization with Stochastic Oracles

We first formalize stochastic optimization in the oracle-versus-optimizer paradigm (Nemirovski and Yudin, 1983) in Section 1, and then sketch the state-of-the-art upper and lower bounds for the rate of convergence (Agarwal et al., 2009) in Section 2. Intuitively, they show that there exists a firstorder stochastic oracle (which returns a noisy version of the gradient with zero mean and bounded ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Quantum

سال: 2020

ISSN: 2521-327X

DOI: 10.22331/q-2020-01-13-220