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Author: Gasnikov, Alexander × Start date: 2020 × Has files: Yes × Publisher: Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik × WGL Institute: WIAS ×

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Now showing 1 - 10 of 20
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    Inexact tensor methods and their application to stochastic convex optimization
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2021) Agafonov, Artem; Kamzolov, Dmitry; Dvurechensky, Pavel; Gasnikov, Alexander
    We propose a general non-accelerated tensor method under inexact information on higher- order derivatives, analyze its convergence rate, and provide sufficient conditions for this method to have similar complexity as the exact tensor method. As a corollary, we propose the first stochastic tensor method for convex optimization and obtain sufficient mini-batch sizes for each derivative.
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    Alternating minimization methods for strongly convex optimization
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Tupitsa, Nazarii; Dvurechensky, Pavel; Gasnikov, Alexander
    We consider alternating minimization procedures for convex optimization problems with variable divided in many block, each block being amenable for minimization with respect to its variable with freezed other variables blocks. In the case of two blocks, we prove a linear convergence rate for alternating minimization procedure under Polyak-Łojasiewicz condition, which can be seen as a relaxation of the strong convexity assumption. Under strong convexity assumption in many-blocks setting we provide an accelerated alternating minimization procedure with linear rate depending on the square root of the condition number as opposed to condition number for the non-accelerated method.
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    Adaptive gradient descent for convex and non-convex stochastic optimization
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2019) Ogaltsov, Aleksandr; Dvinskikh, Darina; Dvurechensky, Pavel; Gasnikov, Alexander; Spokoiny, Vladimir
    In this paper we propose several adaptive gradient methods for stochastic optimization. Our methods are based on Armijo-type line search and they simultaneously adapt to the unknown Lipschitz constant of the gradient and variance of the stochastic approximation for the gradient. We consider an accelerated gradient descent for convex problems and gradient descent for non-convex problems. In the experiments we demonstrate superiority of our methods to existing adaptive methods, e.g. AdaGrad and Adam.
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    Oracle complexity separation in convex optimization
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Ivanova, Anastasiya; Gasnikov, Alexander; Dvurechensky, Pavel; Dvinskikh, Darina; Tyurin, Alexander; Vorontsova, Evgeniya; Pasechnyuk, Dmitry
    Ubiquitous in machine learning regularized empirical risk minimization problems are often composed of several blocks which can be treated using different types of oracles, e.g., full gradient, stochastic gradient or coordinate derivative. Optimal oracle complexity is known and achievable separately for the full gradient case, the stochastic gradient case, etc. We propose a generic framework to combine optimal algorithms for different types of oracles in order to achieve separate optimal oracle complexity for each block, i.e. for each block the corresponding oracle is called the optimal number of times for a given accuracy. As a particular example, we demonstrate that for a combination of a full gradient oracle and either a stochastic gradient oracle or a coordinate descent oracle our approach leads to the optimal number of oracle calls separately for the full gradient part and the stochastic/coordinate descent part.
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    On primal and dual approaches for distributed stochastic convex optimization over networks
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Dvinskikh, Darina; Gorbunov, Eduard; Gasnikov, Alexander; Dvurechensky, Alexander; Uribe, César A.
    We introduce a primal-dual stochastic gradient oracle method for distributed convex optimization problems over networks. We show that the proposed method is optimal in terms of communication steps. Additionally, we propose a new analysis method for the rate of convergence in terms of duality gap and probability of large deviations. This analysis is based on a new technique that allows to bound the distance between the iteration sequence and the optimal point. By the proper choice of batch size, we can guarantee that this distance equals (up to a constant) to the distance between the starting point and the solution.
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    Inexact model: A framework for optimization and variational inequalities
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Stonyakin, Fedor; Gasnikov, Alexander; Tyurin, Alexander; Pasechnyuk, Dmitry; Agafonov, Artem; Dvurechensky, Pavel; Dvinskikh, Darina; Piskunova, Victorya
    In this paper we propose a general algorithmic framework for first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems and variational inequalities. This framework allows to obtain many known methods as a special case, the list including accelerated gradient method, composite optimization methods, level-set methods, proximal methods. The idea of the framework is based on constructing an inexact model of the main problem component, i.e. objective function in optimization or operator in variational inequalities. Besides reproducing known results, our framework allows to construct new methods, which we illustrate by constructing a universal method for variational inequalities with composite structure. This method works for smooth and non-smooth problems with optimal complexity without a priori knowledge of the problem smoothness. We also generalize our framework for strongly convex objectives and strongly monotone variational inequalities.
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    Inexact relative smoothness and strong convexity for optimization and variational inequalities by inexact model
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Stonyakin, Fedor; Gasnikov, Alexander; Tyurin, Alexander; Pasechnyuk, Dmitry; Agafonov, Artem; Dvurechensky, Pavel; Dvinskikh, Darina; Artamonov, Sergei; Piskunova, Victorya
    In this paper we propose a general algorithmic framework for first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems and variational inequalities. This framework allows to obtain many known methods as a special case, the list including accelerated gradient method, composite optimization methods, level-set methods, Bregman proximal methods. The idea of the framework is based on constructing an inexact model of the main problem component, i.e. objective function in optimization or operator in variational inequalities. Besides reproducing known results, our framework allows to construct new methods, which we illustrate by constructing a universal conditional gradient method and universal method for variational inequalities with composite structure. These method works for smooth and non-smooth problems with optimal complexity without a priori knowledge of the problem smoothness. As a particular case of our general framework, we introduce relative smoothness for operators and propose an algorithm for VIs with such operator. We also generalize our framework for relatively strongly convex objectives and strongly monotone variational inequalities.
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    Optimal decentralized distributed algorithms for stochastic convex optimization
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Gorbunov, Eduard; Dvinskikh, Darina; Gasnikov, Alexander
    We consider stochastic convex optimization problems with affine constraints and develop several methods using either primal or dual approach to solve it. In the primal case we use special penalization technique to make the initial problem more convenient for using optimization methods. We propose algorithms to solve it based on Similar Triangles Method with Inexact Proximal Step for the convex smooth and strongly convex smooth objective functions and methods based on Gradient Sliding algorithm to solve the same problems in the non-smooth case. We prove the convergence guarantees in smooth convex case with deterministic first-order oracle. We propose and analyze three novel methods to handle stochastic convex optimization problems with affine constraints: SPDSTM, R-RRMA-AC-SA and SSTM_sc. All methods use stochastic dual oracle. SPDSTM is the stochastic primal-dual modification of STM and it is applied for the dual problem when the primal functional is strongly convex and Lipschitz continuous on some ball. R-RRMA-AC-SA is an accelerated stochastic method based on the restarts of RRMA-AC-SA and SSTM_sc is just stochastic STM for strongly convex problems. Both methods are applied to the dual problem when the primal functional is strongly convex, smooth and Lipschitz continuous on some ball and use stochastic dual first-order oracle. We develop convergence analysis for these methods for the unbiased and biased oracles respectively. Finally, we apply all aforementioned results and approaches to solve decentralized distributed optimization problem and discuss optimality of the obtained results in terms of communication rounds and number of oracle calls per node.
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    Convex optimization with inexact gradients in Hilbert space and applications to elliptic inverse problems
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2021) Matyukhin, Vladislav; Kabanikhin, Sergey; Shishlenin, Maxim; Novikov, Nikita; Vasin, Artem; Gasnikov, Alexander
    In this paper we propose the gradient descent type methods to solve convex optimization problems in Hilbert space. We apply it to solve ill-posed Cauchy problem for Poisson equation and make a comparative analysis with Landweber iteration and steepest descent method. The theoretical novelty of the paper consists in the developing of new stopping rule for accelerated gradient methods with inexact gradient (additive noise). Note that up to the moment of stopping the method ``doesn't feel the noise''. But after this moment the noise start to accumulate and the quality of the solution becomes worse for further iterations.
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    On accelerated alternating minimization
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020) Guminov, Sergey; Dvurechensky, Pavel; Gasnikov, Alexander
    Alternating minimization (AM) optimization algorithms have been known for a long time and are of importance in machine learning problems, among which we are mostly motivated by approximating optimal transport distances. AM algorithms assume that the decision variable is divided into several blocks and minimization in each block can be done explicitly or cheaply with high accuracy. The ubiquitous Sinkhorn's algorithm can be seen as an alternating minimization algorithm for the dual to the entropy-regularized optimal transport problem. We introduce an accelerated alternating minimization method with a $1/k^2$ convergence rate, where $k$ is the iteration counter. This improves over known bound $1/k$ for general AM methods and for the Sinkhorn's algorithm. Moreover, our algorithm converges faster than gradient-type methods in practice as it is free of the choice of the step-size and is adaptive to the local smoothness of the problem. We show that the proposed method is primal-dual, meaning that if we apply it to a dual problem, we can reconstruct the solution of the primal problem with the same convergence rate. We apply our method to the entropy regularized optimal transport problem and show experimentally, that it outperforms Sinkhorn's algorithm.