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Everything about Sequential quadratic programming
Optimization algorithm
Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization, also known as Lagrange-Newton method. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Sequential quadratic programmingA 5-minute orientation
- Skim a recent paper: Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus modelInfluential work since 2022
- Pick up a classic book: Numerical optimizationThe most-reprinted book on the subject
- Read the landmark paper: Sequential Quadratic ProgrammingCited 1,508 times
- Go sideways: Sequential linear-quadratic programmingA closely related topic
- Go sideways: Mehrotra predictor–corrector methodA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Sequential Quadratic Programming
Sequential quadratic programming for large-scale nonlinear optimization
A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
A robust sequential quadratic programming method
Path-constrained trajectory optimization using sparse sequential quadratic programming
Stabilized Sequential Quadratic Programming
On the Sequential Quadratically Constrained Quadratic Programming Methods
VAR compensation by sequential quadratic programming
🆕 Influential since 2022
Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model
An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians
Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming
Posture Optimization in Robotic Flat-End Milling Based on Sequential Quadratic Programming
A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization
đź“š Books worth reading
Numerical optimizationJ. Frédéric Bonnans, Jean Charles Gilbert · 2003- 📕Structural optimization with approximate sensitivitiesSurya N. Patnaik · 1994
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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- Regularization by spectral filtering Spectral regularization is any of a class of regularization techniques used in machine learning to control the impact of noise and prevent overfitting…
- Karush–Kuhn–Tucker conditions In mathematical optimization, the Karush–Kuhn–Tucker (KKT) conditions, also known as the Kuhn–Tucker conditions, are first derivative tests (sometimes…