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# Xpress NonLinear multistart

The feature is an additive feature that minimizes the development overhead and effort of implementing parallel multistart searches. The purpose of multistart is two-fold. Traditionally, multistart is a so called "globalization" feature. It is important to correctly understand what this technology offers, and what it does not. It offers a convenient and efficient way of exploring a larger feasible space building on top of existing local solver algorithms by the means of perturbing initial points and/or parameters or even the problem statement itself. Multistarts increase the chance that one of the found local optima is indeed the global one. However, multistarts do not guarantee that a global optimum is found and never provide a proof of global optimality. For solving MINLPs to proven global optimality, consider using FICO Xpress Global.

Multistart can also be viewed as a left-alone feature. In a typical situation, versions of a model react favourably to a set of control settings, dependent on data. Multistart allows for a simple way of combining different control setting scenarios, increasing the robustness of the model.

The initial problem is defined as the baseline: as the model is normally loaded it without any multistart information, including problem description, callbacks and controls. A run or a job is defined as a problem instance that needs to be solved as part of multistart.

On completion, the initial problem is set up to match that of the winner, allowing examination of the winning strategy and solution using the normal means.

The initial prob object is not reused, all runs are made on a copy of the problem, allowing full customization from the callbacks, including changes to structure.

Callbacks are inherited by the multistart jobs from the initial problem and can be customized from the multistart callbacks. XSLPinterrupt has a global scope, and calling it terminates the multistart search.

Although not intended as the primary use, multistart allows the execution of all supported problem classes, so for example alternate MIP strategies can be used in parallel.

The multistart job pool is maintained and can be extended until the first call to optimize / nlpoptimize with XSLP_MULTISTART on. This allows for doing optimizations runs aimed at generating multistart jobs. The multistart pool is dynamic and new jobs can be added on the fly from the jobstart and jobend callbacks.

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