We propose and analyze a pseudo-transient continuation algorithm for dynamics on subsets of R . Examples include certain flows on manifolds and the dynamic formulation of bound-constrained optimization problems. The method gets its global convergence properties from the dynamics and inherits its local convergence properties from any fast locally convergent iteration.

In this paper we formulate three classes of optimization problems: the simple, monotonically-constrained, and bounded CH-programs. We reveal the dominance property under the local re nement (LR) operation for the simple CH-program, as well as the general dominance property under the pseudo-LR operation for the monotonically-constrained CH-program and the extended-LR operation for the bounded CH...

Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using classical optimization methods, this paper presents a Multi-Objective Bees Algorithm (MOBA) for solving the multi-objective optimal of mechanical engineering problems design. In the pre...

We examine the problem of transmembrane protein structure determination. Like many other questions that arise in biological research, this problem cannot be addressed by traditional laboratory experimentation alone. An approach that integrates experiment and computation is required. We investigate a procedure which states the transmembrane protein structure determination problem as a bound cons...

W examine the problem of transmembrane protein structure determination. Like many questions that arise in biological research, this problem cannot be addressed generally by traditional laboratory experimentation alone. Instead, an approach that integrates experiment and computation is required. We formulate the transmembrane protein structure determination problem as a bound-constrained optimiz...

Error bound analysis, which estimates the distance of a point to solution set an optimization problem4 using optimality residual, is powerful tool for analysis first-order algorithms. In this paper, we use global error study iteration complexity algorithm linearly constrained nonconvex minimization problem. We develop dual regularized version problem by novel “decomposition” technique. Equipped...

-Recurrent neural networks (RNNs) with linearized dynamics have shown great promise in solving continuous valued optimization problems subject to bound constraints. Building on this progress, a novel method of constrained hierarchical multi-scale optimization is developed that applies to a wide range of optimization problems and signal decomposition tasks. Central to the underlying concept is t...

We describe a new implementation of an interval optimization algorithm with focus on the software related issues. The algorithm implemented in MATLAB that uses the INTLAB package supporting interval calculations and automatic differentiation solves the bound constrained global optimization problem. The method itself is a simplified version of those interval techniques much investigated in the p...

A new approach to solve Chance constrained Portfolio Optimization Problems (CPOPs) without using the Monte Carlo simulation is proposed. Specifically, according to Chebyshev inequality, the prediction interval of a stochastic function value included in CPOP is estimated from a set of samples. By using the prediction interval, CPOP is transformed into Lower-bound Portfolio Optimization Problem (...