Results 201 to 210 of about 3,986 (259)

Hybrid quantum-classical stochastic programming for co-planning 5G base stations and photovoltaic power stations in urban communities. [PDF]

Sci Rep
Xu Y, Han X, Luo R, Li Z.
europepmc   +1 more source

A Structural Optimization Framework for Biodegradable Magnesium Interference Screws. [PDF]

Biomimetics (Basel)
Shen Z, Zhou X, Zhao M, Li Y.
europepmc   +1 more source

Accelerated inference for stochastic compartmental models with over-dispersed partial observations. [PDF]

Stat Comput
Whitehouse M.
europepmc   +1 more source

On the Sequential Quadratically Constrained Quadratic Programming Methods [PDF]

Mathematics of Operations Research, 2004
An iteration of the sequential quadratically constrained quadratic programming method (SQCQP) consists of minimizing a quadratic approximation of the objective function subject to quadratic approximation of the constraints, followed by a line search in the obtained direction.
M V Solodov
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Sequential Quadratic Programming with Penalization of the Displacement

SIAM Journal on Optimization, 1995
Summary: We study the convergence of a sequential quadratic programming algorithm for the nonlinear programming problem. The Hessian of the quadratic program is the sum of an approximation of the Lagrangian and of a multiple of the identity that allows us to penalize the displacement.
J. Frédéric Bonnans, Geneviève Launay
openaire   +2 more sources

A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm [PDF]

SIAM Journal on Optimization, 2001
Summary: A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduction in the amount of computation required to generate a new iterate while the proposed scheme still enjoys the same global and ...
André Tits
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Sequential Quadratic Programming

Acta Numerica, 1995
Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved.
Paul T. Boggs, Jon W. Tolle
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A robust sequential quadratic programming method

Mathematical Programming, 1989
The authors propose a modification of the SQP-approach for solving nonlinear programming problems. It is well-known that in the classical approach developed by Wilson, Han and Powell the quadratic subproblems can be infeasible. To overcome this drawback such QP-subproblems are defined, in which the right-hand vector of the constraints ensures ...
James V. Burke, Shih-Ping Han
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Stabilized Sequential Quadratic Programming

Computational Optimization and Applications, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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