Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite, number of loss functions. In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space. To this end, we show the developments on the Grassmann manifold. The key challenges of...

this paper proposes the exchange market algorithm (ema) to solve the combined economic and emission dispatch (ceed) problems in thermal power plants. the ema is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. existence of two seeking operators in ema provides a high ability in exploiting global optimum point. in order to show the capabilities ...

The spectral gradient method has proved to be effective for solving large-scale unconstrained optimization problems. It has been recently extended and combined with the projected gradient method for solving optimization problems on convex sets. This combination includes the use of nonmonotone line search techniques to preserve the fast local convergence. In this work we further extend the spect...

In this paper we propose a subspace limited memory quasi-Newton method for solving large-scale optimization with simple bounds on the variables. The limited memory quasi-Newton method is used to update the variables with indices outside of the active set, while the projected gradient method is used to update the active variables. The search direction consists of three parts: a subspace quasi-Ne...

Abstract Stochastic Gradient Algorithms (SGAs) are ubiquitous in computational statistics, machine learning and optimisation. Recent years have brought an influx of interest SGAs, the non-asymptotic analysis their bias is by now well-developed. However, relatively little known about optimal choice random approximation (e.g mini-batching) gradient SGAs as this relies on variance problem specific...

Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known reinforcement learning methods. However, this method inefficient and unstable in practical applications. On other hand, bias variance Q estimation target function are sometimes difficult to control. This paper proposes a Regularly Updated (RUD) policy gradient for these problems. theoretically proves that procedur...

In this paper, a novel template matching algorithm named radial ring code histograms(RRCH) for multi-objects positioning is proposed. It is invariant to translation, rotation and illumination changes. To improve the identification ability of multi objects with different rotation angles, radial gradient codes using relative angle between gradient direction and position vector is proposed. Adjust...