Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

We present a strikingly simple proof that two rules are sufficient to automate gradient
descent: 1) don't increase the stepsize too fast and 2) don't overstep the local curvature. No …

Composite optimization offers a powerful modeling tool for a variety of applications and is
often numerically solved by means of proximal gradient methods. In this paper, we consider …

Tseng's algorithm finds a zero of the sum of a maximally monotone operator and a
monotone continuous operator by evaluating the latter twice per iteration. In this paper, we …

In this paper, we explore two fundamental first-order algorithms in convex optimization,
namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on …

In this paper, our interest is in investigating the monotone inclusion problems in the
framework of real Hilbert spaces. To solve this problem, we propose a new modified forward …

In optimization theory, convex minimization problems have been intensively investigated in
the current literature due to its wide range in applications. A major and effective tool for …

The forward–backward algorithm is a splitting method for solving convex minimization
problems of the sum of two objective functions. It has a great attention in optimization due to …

We study the variable metric forward-backward splitting algorithm for convex minimization
problems without the standard assumption of the Lipschitz continuity of the gradient. In this …

The paper concerns with novel first-order methods for monotone variational inequalities.
They use a very simple linesearch procedure that takes into account a local information of …