<div dir="ltr">Dear,<div><br></div><div style>I've implemented a small NLP model into ipopt using the Java interface. When I hit solve, the program terminates with the following message:</div><div style><br></div><div style>
"The problem FAILED".</div><div style><br></div><div style>I'm trying to debug the model to see what's wrong. Hence, I've set the objective function to a fixed constant, thereby turning the optimization problem into a constraint satisfaction problem. Next, I've initialized the problem with a feasible solution, i.e. a solution which satisfies all constraints. So basically, the optimizer could return the initial solution immediately; no optimization is required. Still, I get the message that the problem fails (part of the log is included at the end of this mail). </div>
<div style><br></div><div style>1. Why doesn't the solver return the initial solution, as it is a feasible solution?</div><div style><br></div><div style>2. What could cause ipopt to return 'The problem Failed', i.e. what should I debug? I manually checked that all my constraints are feasible, as well as the variable bounds.</div>
<div style><br></div><div style>3. The logger mentions that the search direction becomes too small. Why can't it identify that it has found a local/global optimum?</div><div style><br></div><div style>br,</div><div style>
<br></div><div style>Joris</div><div style><br></div><div style><br></div><div style><br></div><div style>===============================</div><div style><br></div><div style><div>Number of Iterations....: 263</div><div><br>
</div><div> (scaled) (unscaled)</div><div>Objective...............: 1.0000000000000000e+00 1.0000000000000000e+00</div><div>Dual infeasibility......: 1.0999505370964350e-05 1.0999505370964350e-05</div>
<div>Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00</div><div>Complementarity.........: 9.0909090909090941e-10 9.0909090909090941e-10</div><div>Overall NLP error.......: 1.0999505370964350e-05 1.0999505370964350e-05</div>
<div><br></div><div><br></div><div>Number of objective function evaluations = 289</div><div>Number of objective gradient evaluations = 55</div><div>Number of equality constraint evaluations = 0</div>
<div>Number of inequality constraint evaluations = 289</div><div>Number of equality constraint Jacobian evaluations = 0</div><div>Number of inequality constraint Jacobian evaluations = 266</div><div>Number of Lagrangian Hessian evaluations = 264</div>
<div>Total CPU secs in IPOPT (w/o function evaluations) = 0.180</div><div>Total CPU secs in NLP function evaluations = 0.016</div><div><br></div><div>EXIT: Search Direction is becoming Too Small.</div>
<div>Obj: 1.0</div><div>Circle 0: (-41.30054873354737,3.222049686239637), r:10.0</div><div>Circle 1: (0.49442322310794523,-26.773758302392906), r:10.0</div><div>Circle 2: (-44.843610818313735,-23.277936648765795), r:10.0</div>
<div>Circle 3: (-22.36952325252463,-36.936746537913145), r:10.0</div><div><br></div><div><br></div><div>*** The problem FAILED!</div></div></div>