Sequential quadratic programming (SQP : 비선형 최적화 알고리즘) is an iterative method for constrained nonlinear optimization. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable.

SQP methods solve a sequence of optimization subproblems, each of which optimizes a quadratic model of the objective subject to a linearization of the constraints. If the problem is unconstrained, then the method reduces to Newton’s method for finding a point where the gradient of the objective vanishes. If the problem has only equality constraints, then the method is equivalent to applying Newton’s method to the first-order optimality conditions, or Karush–Kuhn–Tucker conditions, of the problem.

Sequential quadratic programming 문제를 푸는 것은 GM, LM과 같은 여러 방법들이 있다.

**블로그에 Non Linear Least Square 검색하여서 GM과 LM에 더 구체적으로 이해할 수 있다.