59,355 research outputs found
A second derivative SQP method: theoretical issues
Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding their global solutions may be computationally nonviable. This paper presents a second-derivative SQP method based on quadratic subproblems that are either convex, and thus may be solved efficiently, or need not be solved globally. Additionally, an explicit descent-constraint is imposed on certain QP subproblems, which âguidesâ the iterates through areas in which nonconvexity is a concern. Global convergence of the resulting algorithm is established
Second-Order Karush-Kuhn-Tucker Optimality Conditions for Vector Problems with Continuously Differentiable Data and Second-Order Constraint Qualifications
Some necessary and sufficient optimality conditions for inequality
constrained problems with continuously differentiable data were obtained in the
papers [I. Ginchev and V.I. Ivanov, Second-order optimality conditions for
problems with C\sp{1} data, J. Math. Anal. Appl., v. 340, 2008, pp.
646--657], [V.I. Ivanov, Optimality conditions for an isolated minimum of order
two in C\sp{1} constrained optimization, J. Math. Anal. Appl., v. 356, 2009,
pp. 30--41] and [V. I. Ivanov, Second- and first-order optimality conditions in
vector optimization, Internat. J. Inform. Technol. Decis. Making, 2014, DOI:
10.1142/S0219622014500540].
In the present paper, we continue these investigations. We obtain some
necessary optimality conditions of Karush--Kuhn--Tucker type for scalar and
vector problems. A new second-order constraint qualification of Zangwill type
is introduced. It is applied in the optimality conditions.Comment: 1
A distributionally robust perspective on uncertainty quantification and chance constrained programming
The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and maintain its safety, a scenario which can be modeled through a chance constrained program. In this paper we assume that the parameters of the system are governed by an ambiguous distribution that is only known to belong to an ambiguity set characterized through generalized moment bounds and structural properties such as symmetry, unimodality or independence patterns. We delineate the watershed between tractability and intractability in ambiguity-averse uncertainty quantification and chance constrained programming. Using tools from distributionally robust optimization, we derive explicit conic reformulations for tractable problem classes and suggest efficiently computable conservative approximations for intractable ones
Discrete mechanics and optimal control for constrained systems
The equations of motion of a controlled mechanical system subject to holonomic constraints may be formulated in terms
of the states and controls by applying a constrained version of the Lagrange-dâAlembert principle. This paper derives a
structure-preserving scheme for the optimal control of such systems using, as one of the key ingredients, a discrete analogue
of that principle. This property is inherited when the system is reduced to its minimal dimension by the discrete null
space method. Together with initial and final conditions on the configuration and conjugate momentum, the reduced discrete
equations serve as nonlinear equality constraints for the minimization of a given objective functional. The algorithm yields
a sequence of discrete configurations together with a sequence of actuating forces, optimally guiding the system from the
initial to the desired final state. In particular, for the optimal control of multibody systems, a force formulation consistent
with the joint constraints is introduced. This enables one to prove the consistency of the evolution of momentum maps.
Using a two-link pendulum, the method is compared with existing methods. Further, it is applied to a satellite reorientation
maneuver and a biomotion problem
A variational framework for flow optimization using semi-norm constraints
When considering a general system of equations describing the space-time
evolution (flow) of one or several variables, the problem of the optimization
over a finite period of time of a measure of the state variable at the final
time is a problem of great interest in many fields. Methods already exist in
order to solve this kind of optimization problem, but sometimes fail when the
constraint bounding the state vector at the initial time is not a norm, meaning
that some part of the state vector remains unbounded and might cause the
optimization procedure to diverge. In order to regularize this problem, we
propose a general method which extends the existing optimization framework in a
self-consistent manner. We first derive this framework extension, and then
apply it to a problem of interest. Our demonstration problem considers the
transient stability properties of a one-dimensional (in space) averaged
turbulent model with a space- and time-dependent model "turbulent viscosity".
We believe this work has a lot of potential applications in the fluid
dynamics domain for problems in which we want to control the influence of
separate components of the state vector in the optimization process.Comment: 30 page
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