Add knowledge-gradient and noisy-EI acquisition functions

[MarcusMNoack]

Two lookahead optimization acquisitions, available as built-in strings for both single-task (GPOptimizer) and multi-task (fvGPOptimizer) decision-making:

• "knowledge gradient": expected increase in the maximum of the posterior mean after a fantasized measurement (KGCP scheme; reference set = data points plus the candidate). The correlated-KG expectation E[max_i a_i + b_i Z] is computed exactly via the upper envelope of lines (Frazier, Powell & Dayanik 2009), validated against Monte Carlo.
• "noisy expected improvement": EI averaged over the posterior of the incumbent, making it robust to observation noise (Letham et al. 2019). Monte-Carlo over posterior samples of the objective at the observed points, using common random numbers so the score is smooth for the acquisition optimizer.

For multi-task GPs both act on the task-summed objective sum_t f(x, t), matching how the existing multi-task expected improvement / ucb scalarize the outputs. The scalarized cross-covariance is taken from posterior_covariance()["S_flat"] using the task-major (k = point + Npts*task) product-space ordering.

Both are tunable through the constructor args dict (reference-set size, seed, and MC sample count for NEI). Wired into evaluate_gp_acquisition_function on both branches, the ask() docstring, tests (single and multi-task), and the acquisition-functions skill.

[codecov]

Codecov Report

❌ Patch coverage is 92.12598% with 10 lines in your changes missing coverage. Please review.
✅ Project coverage is 90.05%. Comparing base (b5e9bd2) to head (78f4d3c).
⚠️ Report is 3 commits behind head on master.

Files with missing lines	Patch %	Lines
gpcam/surrogate_model.py	91.30%	10 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master      #54      +/-   ##
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+ Coverage   89.74%   90.05%   +0.30%     
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  Files           9        9              
  Lines         819      945     +126     
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+ Hits          735      851     +116     
- Misses         84       94      +10     

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