.. Auto-generated by scripts/generate_problem_catalog_docs.py. Do not edit by hand.

Generalized Moving Peaks Benchmark (dynamic minimization wrapper)
=================================================================

A stateful dynamic optimization benchmark that wraps GMPB's native maximization objective and negates it to fit this package's minimization-oriented optimization API.

See :doc:`../optimization` for the optimization family index.

Quick Facts
-----------

.. list-table::
   :header-rows: 1
   :widths: 20 80

   * - Field
     - Value
   * - Problem ID
     - ``gmpb_default_dynamic_min``
   * - Problem Family
     - optimization
   * - Implementation
     - ``design_research_problems.problems.optimization._gmpb:GMPBOptimizationProblem``
   * - Capabilities
     - ``baseline-solver``, ``bounded-variables``, ``external-adapter``, ``statement-markdown``
   * - Study Suitability
     - none
   * - Tags
     - ``optimization``, ``continuous``, ``dynamic``, ``benchmark``, ``single-objective``

Taxonomy
--------

Formulation
   nonlinear_program
Convexity
   not_guaranteed
Design Variable Type
   continuous
Is Dynamic
   yes
Orientation
   mathematical
Objective Mode
   single
Constraint Nature
   hard
Bounds Summary
   five continuous variables bounded on [-100, 100]
Tags
   ``optimization``, ``continuous``, ``dynamic``, ``benchmark``, ``single-objective``

Statement
---------

This packaged optimization problem wraps the external Generalized Moving Peaks Benchmark (GMPB). GMPB is defined as a dynamic maximization benchmark whose evaluation counter advances through changing environments.

To fit the optimization API in this package, the wrapper reports the negated native GMPB objective, so lower values are better. Evaluations remain stateful: each call consumes benchmark budget in the current environment, and the environment changes automatically when the configured change frequency is exhausted.

Problem Shape
-------------

.. list-table::
   :header-rows: 1
   :widths: 30 70

   * - Field
     - Value
   * - Design Variable Count
     - 5
   * - Bound Summary
     - five continuous variables bounded on [-100, 100]
   * - Total Constraint Count
     - 0
   * - Equality Constraint Count
     - 0
   * - Inequality Constraint Count
     - 0

Variable Bounds
---------------

.. list-table::
   :header-rows: 1
   :widths: 25 37 38

   * - Variable
     - Lower Bound
     - Upper Bound
   * - ``x[0]``
     - -100
     - 100
   * - ``x[1]``
     - -100
     - 100
   * - ``x[2]``
     - -100
     - 100
   * - ``x[3]``
     - -100
     - 100
   * - ``x[4]``
     - -100
     - 100

Manifest Parameters
-------------------

.. list-table::
   :header-rows: 1
   :widths: 25 75

   * - Key
     - Value
   * - change_frequency
     - 1000
   * - component_count
     - 10
   * - dimension
     - 5
   * - environment_count
     - 100
   * - lower_bound
     - -100
   * - seed
     - 7
   * - upper_bound
     - 100

Library Interface
-----------------

- ``generate_initial_solution(seed=None)``
- ``objective(x)``
- ``evaluate(x)``
- ``solve(initial_solution=None, seed=None, maxiter=200)``