The main aim of this paper is to extend the single-agent policy gradient method for multiagent domains where all agents share the same utility function. We formulate these team problems as Markov games endowed with the asymmetric equilibrium concept and based on this formulation, we provide a direct policy gradient learning method. In addition, we test the proposed method with a small example p...

We develop an accelerated randomized proximal coordinate gradient (APCG) method, for solving a broad class of composite convex optimization problems. In particular, our method achieves faster linear convergence rates for minimizing strongly convex functions than existing randomized proximal coordinate gradient methods. We show how to apply the APCG method to solve the dual of the regularized em...

Using Taylor's series we propose a modified secant relation to get a more accurate approximation of the second curvature of the objective function. Then, based on this modified secant relation we present a new BFGS method for solving unconstrained optimization problems. The proposed method make use of both gradient and function values while the usual secant relation uses only gradient values. U...

A global convergence theorem for unconstrained minimization algorithms with an eecient line search is derived. The theorem applies to a new version of the conjugate gradient method derived here in terms of minimizing the eeect of zigzagging. The global convergence condition makes much weaker demands on the line search than previous methods. Local Q-linear convergence in a neighborhood of a stro...

A method for calculating the analytical energy gradient of the ground, excited, ionized, and electron-attached states Ž . Ž . calculated by the SAC symmetry-adapted cluster rSAC–CI configuration interaction method was formulated and implemented. This method adapts to the selection procedure of the linked and unlinked operators in the current SACrSAC–CI code. It was applied to various molecules ...

We describe our investigation of a fast 3D finite element method (FEM) for biomedical simulation of a muscle-activated human tongue. Our method uses a linear stiffness-warping scheme to achieve simulation speeds which are within a factor 10 of real-time rates at the expense of a small loss in accuracy. Muscle activations are produced by an arrangement of forces acting along selected edges of th...

The classical way of solving the time-harmonic linear acousto-elastic wave problem is to discretize the equations with finite elements or finite differences. This approach leads to large-scale indefinite complex-valued linear systems. For this kind of systems, it is difficult to construct efficient iterative solution methods. That is why we use an alternative approach and solve the time-harmoni...

We apply the holonomic gradient method to compute the distribution function of a weighted sum of independent noncentral chi-square random variables. It is the distribution function of the squared length of a multivariate normal random vector. We treat this distribution as an integral of the normalizing constant of the Fisher-Bingham distribution on the unit sphere and make use of the partial di...

The profile gradient method has been recently introduced as a means of generating screw compressor rotor profiles. As a single parameter method, this procedure is convenient for the optimisation of screw compressor rotors and evaluating their quality. In this paper, the procedure is modified to adopt the pressure angle instead the profile gradient and applied to a screw compressor rotor rack. O...

The traditional paradigm in machine learning has been that given a data set, the goal is to learn a target function or decision model (such as a classifier) from it. Many techniques in data mining and machine learning follow a gradient descent paradigm in the iterative process of discovering this target function or decision model. For instance, Linear regression can be resolved through a gradie...