We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
This work shows asymptotic convergence to global optima for a family of dynamically scaled genetic programming systems where the underlying population consists of a fixed number o...
Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
This paper presents and compares results for three types of connectionist networks on perceptual learning tasks: [A] Multi-layered converging networks of neuron-like units, with e...
Abstract. Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second deri...