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I try to optimize a nonlinear problem with Ipopt (v0.6.1) using JuMP (v0.20.1). When the algorithm terminates, the output message is: “EXIT: Restoration Failed! Restoration phase converged to a feasible point that is. unacceptable to the filter for the original problem. Restoration phase in the restoration phase failed.”.

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Project Restoration (Phase Three in the works!) ... Thank you so much for expessing your appreciation and respect for our ongoing efforts regarding Project Restoration. It is a baby of mine, carried out by the wonderful members you've seen on this thread, and a profound expression of cooperation and teamwork that, at times, has left me quite.

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0001 % IPOPT Call the IPOPT constrained, nonlinear solver. 0002 % The basic function call is 0003 % 0004 % [x, info] = IPOPT(x0,funcs,options) 0005 % 0006 % The first input is either a matrix or a cell array of matrices. It 0007 % declares the starting point for the solver. 0008 % 0009 % CALLBACK FUNCTIONS 0010 % 0011 % The second input must be struct containing.

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Safety factor for the minimal step size (before switching to restoration phase). (see IPOPT documentation) CasADi::IpoptInternal : alpha_red_factor: OT_REAL: 0.5: Fractional reduction of the trial step size in the backtracking line search. (see IPOPT documentation) CasADi::IpoptInternal : barrier_tol_factor: OT_REAL: 10.0: Factor for mu in.

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Here we provide a comprehensive description of the algorithm, including the feasibility restoration phase for the fil- ter method, second-order corrections, and inertia correction of the KKT matrix. Heuristics are also considered that allow faster performance.

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Algorithm phase: 0 is for regular, 1 is restoration. iter_count: The current iteration count. obj_value: ... For example, if this number is chosen to be 10, then Ipopt solves internally an optimization problem that has 10 times the value of the original objective. In particular, if this value is negative, then Ipopt will maximize the objective.

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2. The funcs struct objective Function handle for the objective function. Assignment of a function handle by funcs.objective = @objective; where objective is the name of a valid Matlab function. Signature: function f = objective (x, auxdata) x Current values of the primal variables. Format identical to x0. auxdata (optional) User-defined data, cf. options.auxdata. Presumably, PyPlot isn't needed to show this behavior. As a result, I haven't actually run your code. From the Ipopt docs: Restoration Failed: Console Message: EXIT: Restoration Failed! This indicates that the restoration phase failed to find a feasible point that was acceptable to the. filter line search for the original problem.

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In this context, IPOPT is an interior point filter line-search algorithm for solving large-scale nonlinear optimization problems developed by Wächter and Biegler ( 2001, 2006 ). The idea of this algorithm is to combine the primal-dual interior point algorithms with filter line search for solving problems of the form: \min\ f (x) (20.1a) subject to.

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The incident response phases are: Preparation. Identification. Containment. Eradication. Recovery. Lessons Learned. Let’s look at each phase in more depth and point out the items that you need to address. SEE ALSO: 6 Steps to Making an Incident Response Plan.

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this options is also used when initializing the restoration phase.0589 % by default, "resto.constr_mult_init_max" (the one used in0590 % restoiterateinitializer) is set to zero.0591 %0592 % bound_mult_init_val 0 < ( 1) < +inf0593 % initial value for the bound multipliers.0594 % all dual variables.

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This issue can come up when Ipopt fails to find an improving step at the current iterate. As described in Section 2.3 of the implementation paper, one fallback is to start the feasibility restoration phase.However, if the current iterate is already primal feasible, then there is no feasibility to restore - that's what the message tries to say.

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