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Stabilized sequential quadratic programming for optimization and a stabilized Newton-type method for variational problems. (2010)

by D Fernández, M Solodov
Venue:Math. Program.
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On attraction of linearly constrained Lagrangian methods and of stabilized and quasi-Newton SQP methods to critical multipliers

by A. F. Izmailov, M. V. Solodov - MATHEMATICAL PROGRAMMING , 2009
"... ..."
Abstract - Cited by 17 (10 self) - Add to MetaCart
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LOCAL CONVERGENCE OF EXACT AND INEXACT AUGMENTED LAGRANGIAN METHODS UNDER THE SECOND-ORDER SUFFICIENT OPTIMALITY CONDITION

by D. Fernández, M. V. Solodov , 2012
"... We establish local convergence and rate of convergence of the classical augmented Lagrangian algorithm under the sole assumption that the dual starting point is close to a multiplier satisfying the second-order sufficient optimality condition. In particular, no constraint qualifications of any kind ..."
Abstract - Cited by 16 (5 self) - Add to MetaCart
We establish local convergence and rate of convergence of the classical augmented Lagrangian algorithm under the sole assumption that the dual starting point is close to a multiplier satisfying the second-order sufficient optimality condition. In particular, no constraint qualifications of any kind are needed. Previous literature on the subject required, in addition, the linear independence constraint qualification and either the strict complementarity assumption or a stronger version of the second-order sufficient condition. That said, the classical results allow the initial multiplier estimate to be far from the optimal one, at the expense of proportionally increasing the threshold value for the penalty parameters. Although our primary goal is to avoid constraint qualifications, if the stronger assumptions are introduced, then starting points far from the optimal multiplier are allowed within our analysis as well. Using only the second-order sufficient optimality condition, for penalty parameters large enough we prove primal-dual Q-linear convergence rate, which becomes superlinear if the parameters are allowed to go to infinity. Both exact and inexact solutions of subproblems are considered. In the exact case, we further show that the primal convergence rate is of the same Q-order as the primal-dual rate. Previous assertions for the primal sequence all had to do with the weaker R-rate of convergence and required the stronger assumptions cited above. Finally, we show that under our assumptions one of the popular rules of controlling the penalty parameters ensures their boundedness.

REGULARIZED SEQUENTIAL QUADRATIC PROGRAMMING METHODS

by Philip E. Gill, et al. , 2011
"... We present the formulation and analysis of a new sequential quadratic programming (SQP) method for general nonlinearly constrained optimization. The method pairs a primal-dual generalized augmented Lagrangian merit function with a flexible line search to obtain a sequence of improving estimates of t ..."
Abstract - Cited by 15 (3 self) - Add to MetaCart
We present the formulation and analysis of a new sequential quadratic programming (SQP) method for general nonlinearly constrained optimization. The method pairs a primal-dual generalized augmented Lagrangian merit function with a flexible line search to obtain a sequence of improving estimates of the solution. This function is a primal-dual variant of the augmented Lagrangian proposed by Hestenes and Powell in the early 1970s. A crucial feature of the method is that the QP subproblems are convex, but formed from the exact second derivatives of the original problem. This is in contrast to methods that use a less accurate quasi-Newton approximation. Additional benefits of this approach include the following: (i) each QP subproblem is regularized; (ii) the QP subproblem always has a known feasible point; and (iii) a projected gradient method may be used to identify the QP active set when far from the solution.

INEXACT JOSEPHY–NEWTON FRAMEWORK FOR GENERERALIZED EQUATIONS AND ITS APPLICATIONS TO LOCAL ANALYSIS OF NEWTONIAN METHODS FOR CONSTRAINED OPTIMIZATION ∗

by A. F. Izmailov, M. V. Solodov , 2008
"... We propose and analyze a perturbed version of the classical Josephy-Newton method for solving generalized equations. This perturbed framework is convenient to treat in a unified way standard sequential quadratic programming, its stabilzed version, sequential quadratically constrained quadratic progr ..."
Abstract - Cited by 13 (8 self) - Add to MetaCart
We propose and analyze a perturbed version of the classical Josephy-Newton method for solving generalized equations. This perturbed framework is convenient to treat in a unified way standard sequential quadratic programming, its stabilzed version, sequential quadratically constrained quadratic programming, and linearly constrained Lagrangian methods. For the linearly constrained Lagrangian methods, in particular, we obtain superlinear convergence under the second-order sufficient optimality condition and the strict Mangasarian–Fromovitz constraint qualification, while previous results in the literature assume (in addition to secondorder sufficiency) the stronger linear independence constraint qualification as well as the strict complementarity condition. For the sequential quadratically constrained quadratic programming methods, we prove primal-dual superlinear/quadratic convergence under the same assumptions as above, which also gives a new result.
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...for optimization. In what follows, we shall extend the framework for dealing with Newton-related algorithms that can be regarded as inexact JNM (iJNM). These will include the stabilzed version of SQP =-=[35, 16, 13, 34, 33, 12]-=-, sequential quadratically constrained quadratic programming [2, 15, 31, 11], and linearly constrained Lagrangian methods [27, 24, 14]. Formally, instead of (1.2), the next iterate z k+1 would now sat...

A GLOBALLY CONVERGENT STABILIZED SQP METHOD

by Philip E. Gill, et al. , 2013
"... Sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constrained optimization. They are particularly effective for solving a sequence of related problems, such as those arising in mixed-integer nonlinear programming and the optimization of functions subject t ..."
Abstract - Cited by 9 (1 self) - Add to MetaCart
Sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constrained optimization. They are particularly effective for solving a sequence of related problems, such as those arising in mixed-integer nonlinear programming and the optimization of functions subject to differential equation constraints. Recently, there has been considerable interest in the formulation of stabilized SQP methods, which are specifically designed to handle degenerate optimization problems. Existing stabilized SQP methods are essentially local, in the sense that both the formulation and analysis focus on the properties of the methods in a neighborhood of a solution. A new SQP method is proposed that has favorable global convergence properties yet, under suitable assumptions, is equivalent to a variant of the conventional stabilized SQP method in the neighborhood of a solution. The method combines a primal-dual generalized augmented Lagrangian function with a flexible line search to obtain a sequence

SHARP PRIMAL SUPERLINEAR CONVERGENCE RESULTS FOR SOME NEWTONIAN METHODS FOR CONSTRAINED OPTIMIZATION

by D. Fernández, A. F. Izmailov, M. V. Solodov , 2009
"... As is well known, superlinear or quadratic convergence of the primal-dual sequence generated by an optimization algorithm does not, in general, imply superlinear convergence of the primal part. Primal convergence, however, is often of particular interest. For the sequential quadratic programming (SQ ..."
Abstract - Cited by 9 (8 self) - Add to MetaCart
As is well known, superlinear or quadratic convergence of the primal-dual sequence generated by an optimization algorithm does not, in general, imply superlinear convergence of the primal part. Primal convergence, however, is often of particular interest. For the sequential quadratic programming (SQP) algorithm, local primal-dual quadratic convergence can be established under the assumptions of uniqueness of the Lagrange multiplier associated to the solution and the second-order sufficient condition. At the same time, previous primal superlinear convergence results for SQP required to strengthen the first assumption to the linear independence constraint qualification. In this paper, we show that this strengthening of assumptions is actually not necessary. Specifically, we show that once primal-dual convergence is assumed or already established, for primal superlinear rate one only needs a certain error bound estimate. This error bound holds, for example, under the second-order sufficient condition, which is needed for primal-dual local analysis in any case. Moreover, in some situations even second-order sufficiency can be relaxed to the weaker assumption that the multiplier in question is noncritical. Our study is performed for a rather general perturbed SQP framework, which covers in addition to SQP and quasi-Newton SQP some other algorithms as well. For example, as a by-product,
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...n versions of SQP (e.g., [29, 5]), linearly constrained (augmented) Lagrangian methods [30, 25, 13, 21], sequential quadratically constrained quadratic programming [2, 14, 32, 10], and stabilized SQP =-=[34, 15, 12, 36, 11]-=-. It is worth to emphasize once again that for some forms of perturbations, the pSQP framework includes algorithms which may not be modifications of SQP per se, in the sense that subproblems of those ...

A NOTE ON UPPER LIPSCHITZ STABILITY, ERROR BOUNDS, AND CRITICAL MULTIPLIERS FOR LIPSCHITZ-CONTINUOUS KKT SYSTEMS

by A. F. Izmailov, A. S. Kurennoy, M. V. Solodov , 2012
"... We prove a new local upper Lipschitz stability result and the associated local error bound for solutions of parametric Karush–Kuhn–Tucker systems corresponding to variational problems with Lipschitzian base mappings and constraints possessing Lipschitzian derivatives, and without any constraint qual ..."
Abstract - Cited by 8 (5 self) - Add to MetaCart
We prove a new local upper Lipschitz stability result and the associated local error bound for solutions of parametric Karush–Kuhn–Tucker systems corresponding to variational problems with Lipschitzian base mappings and constraints possessing Lipschitzian derivatives, and without any constraint qualifications. This property is equivalent to the appropriately extended to this nonsmooth setting notion of noncriticality of the Lagrange multiplier associated to the primal solution, which is weaker than second-order sufficiency. All this extends several results previously known only for optimization problems with twice differentiable data, or assuming some constraint qualifications. In addition, our results are obtained in the more general variational setting.
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...s implications for convergence of numerical algorithms. For example, SOSC (1.12) was the only assumption needed to prove local convergence of the stabilized sequential quadratic programming method in =-=[5]-=- and of the augmented Lagrangian algorithm in [6], with the error bound (1.8) playing a key role. When 3there are equality constraints only, the error bound itself (equivalently, noncriticality of th...

GLOBAL CONVERGENCE OF AUGMENTED LAGRANGIAN METHODS APPLIED TO OPTIMIZATION PROBLEMS WITH DEGENERATE CONSTRAINTS, INCLUDING PROBLEMS WITH COMPLEMENTARITY CONSTRAINTS

by A. F. Izmailov, M. V. Solodov, E. I. Uskov , 2012
"... We consider global convergence properties of the augmented Lagrangian methods on problems with degenerate constraints, with a special emphasis on mathematical programs with complementarity constraints (MPCC). In the general case, we show convergence to stationary points of the problem under an error ..."
Abstract - Cited by 7 (2 self) - Add to MetaCart
We consider global convergence properties of the augmented Lagrangian methods on problems with degenerate constraints, with a special emphasis on mathematical programs with complementarity constraints (MPCC). In the general case, we show convergence to stationary points of the problem under an error bound condition for the feasible set (which is weaker than constraint qualifications), assuming that the iterates have some modest features of approximate local minimizers of the augmented Lagrangian. For MPCC, we first argue that even weak forms of general constraint qualifications that are suitable for convergence of the augmented Lagrangian methods, such as the recently proposed relaxed positive linear dependence condition, should not be expected to hold and thus special analysis is needed. We next obtain a rather complete picture, showing that under the usual in this context MPCC-linear independence constraint qualification accumulation points of the iterates are guaranteed to be C-stationary for MPCC (better than weakly stationary), but in general need not be M-stationary (hence, neither strongly stationary). However, strong stationarity is guaranteed if the generated dual sequence is bounded, which we show to be the typical
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...ch is confirmed by our numerical results in section 4. It should be mentioned here that a number of special methods for degenerate problems have been developed and analyzed in the last 15 years or so =-=[53, 29, 54, 21, 55, 34, 56, 19, 37]-=-, with stabilized SQP being perhaps the most prominent. According to the analysis in [19], stabilized SQP has the same local convergence properties as the augmented Lagrangian methods, and in particul...

A TRUNCATED SQP METHOD BASED ON INEXACT INTERIOR-POINT SOLUTIONS OF SUBPROBLEMS ∗

by A. F. Izmailov, M. V. Solodov
"... Abstract. We consider sequential quadratic programming (SQP) methods applied to optimization problems with nonlinear equality constraints and simple bounds. In particular, we propose and analyze a truncated SQP algorithm in which subproblems are solved approximately by an infeasible predictor-correc ..."
Abstract - Cited by 6 (5 self) - Add to MetaCart
Abstract. We consider sequential quadratic programming (SQP) methods applied to optimization problems with nonlinear equality constraints and simple bounds. In particular, we propose and analyze a truncated SQP algorithm in which subproblems are solved approximately by an infeasible predictor-corrector interior-point method, followed by setting to zero some variables and some multipliers so that complementarity conditions for approximate solutions are enforced. Verifiable truncation conditions based on the residual of optimality conditions of subproblems are developed to ensure both global and fast local convergence. Global convergence is established under assumptions that are standard for linesearch SQP with exact solution of subproblems. The local superlinear convergence rate is shown under the weakest assumptions that guarantee this property for pure SQP with exact solution of subproblems, namely, the strict Mangasarian–Fromovitz constraint qualification and second-order sufficiency. Local convergence results for our truncated method are presented as a special case of the local convergence for a more general perturbed SQP framework, which is of independent interest and is applicable even to some algorithms whose subproblems are not quadratic programs. For example, the framework can also be used to derive sharp local convergence results for linearly constrained Lagrangian methods. Preliminary numerical results confirm that it can be indeed beneficial to solve subproblems approximately, especially on early iterations. Key words. sequential quadratic programming, inexact sequential quadratic programming, truncated sequential quadratic programming, interior-point method, superlinear convergence
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...). In particular, in addition to tSQP our framework includes quasi-Newton SQP, LCL methods [42, 39, 18], sequential quadratically constrained quadratic programming [1, 19, 45, 15], and stabilized SQP =-=[48, 28, 49, 16]-=-; see [33] and section 4.4 for details. Note that for some of those methods (4.19) is no longer a QP problem, and it is therefore even more natural to expect that the subproblems can be solved only ap...

Stabilized SQP revisited

by A. F. Izmailov, M. V. Solodov - MATH. PROGRAM., SER. A , 2010
"... The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve superlinear convergence in situations when the Lagrange multipliers associated to a solution are not unique. Within the framework of Fischer (Math Program 94:91–124, 2002), the key ..."
Abstract - Cited by 3 (2 self) - Add to MetaCart
The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve superlinear convergence in situations when the Lagrange multipliers associated to a solution are not unique. Within the framework of Fischer (Math Program 94:91–124, 2002), the key to local superlinear convergence of sSQP are the following two properties: upper Lipschitzian behavior of solutions of the Karush-Kuhn-Tucker (KKT) system under canonical perturbations and local solvability of sSQP subproblems with the associated primal-dual step being of the order of the distance from the current iterate to the solution set of the unperturbed KKT system. According to Fernández and Solodov (Math Program 125:47–73, 2010), both of these properties are ensured by the second-order sufficient optimality condition (SOSC) without any constraint qualification assumptions. In this paper, we state precise relationships between the upper Lipschitzian property of solutions of KKT systems, error bounds for KKT systems, the notion of critical Lagrange multipliers (a subclass of multipliers that violate SOSC in a very special way), the second-order necessary condition for optimality, and solvability of sSQP subproblems. Moreover,
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