Advanced statistical and meta-heuristic based optimization fault diagnosis techniques in complex industrial processes: a comparative analysis
Industrial processes are nonlinear and complicated in nature, requiring accurate fault
detection to minimize the deterioration in performance and to respond quickly to …
detection to minimize the deterioration in performance and to respond quickly to …
Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming
We study nonlinear optimization problems with a stochastic objective and deterministic
equality and inequality constraints, which emerge in numerous applications including …
equality and inequality constraints, which emerge in numerous applications including …
Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization
A worst-case complexity bound is proved for a sequential quadratic optimization (commonly
known as SQP) algorithm that has been designed for solving optimization problems …
known as SQP) algorithm that has been designed for solving optimization problems …
A stochastic sequential quadratic optimization algorithm for nonlinear-equality-constrained optimization with rank-deficient Jacobians
A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear-
equality-constrained optimization problems in which the objective function is defined by an …
equality-constrained optimization problems in which the objective function is defined by an …
Inexact sequential quadratic optimization for minimizing a stochastic objective function subject to deterministic nonlinear equality constraints
An algorithm is proposed, analyzed, and tested experimentally for solving stochastic
optimization problems in which the decision variables are constrained to satisfy equations …
optimization problems in which the decision variables are constrained to satisfy equations …
Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction
In this paper, we propose a stochastic method for solving equality constrained optimization
problems that utilizes predictive variance reduction. Specifically, we develop a method …
problems that utilizes predictive variance reduction. Specifically, we develop a method …
A digital economy development index based on an improved hierarchical data envelopment analysis approach
C Guo, Q Song, MM Yu, J Zhang - European Journal of Operational …, 2024 - Elsevier
The digital economy is playing an increasingly important role in the global economy.
National and international organizations commonly utilize a composite index composed of …
National and international organizations commonly utilize a composite index composed of …
Sequential quadratic optimization for stochastic optimization with deterministic nonlinear inequality and equality constraints
A sequential quadratic optimization algorithm for minimizing an objective function defined by
an expectation subject to nonlinear inequality and equality constraints is proposed …
an expectation subject to nonlinear inequality and equality constraints is proposed …
Hessian averaging in stochastic Newton methods achieves superlinear convergence
We consider minimizing a smooth and strongly convex objective function using a stochastic
Newton method. At each iteration, the algorithm is given an oracle access to a stochastic …
Newton method. At each iteration, the algorithm is given an oracle access to a stochastic …
Fully stochastic trust-region sequential quadratic programming for equality-constrained optimization problems
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-
StoSQP) to solve nonlinear optimization problems with stochastic objectives and …
StoSQP) to solve nonlinear optimization problems with stochastic objectives and …