As wind energy technology continues to mature, farm sizes grow and wind farm layout design becomes more difficult, in part due to the number of design variables and constraints. Wind farm layout optimization is typically approached using gradient-free methods because of the highly multi-modal shape of the wind farm layout design space. Gradient-free method performance generally degrades with increasing problem size, making it difficult to find optimal layouts for larger wind farms. However, gradient-based optimization methods can effectively and efficiently solve large-scale problems with many variables and constraints. To pave the way for effective and efficient wind farm layout optimization for large-scale wind farms, we have worked to overcome the primary barriers to applying gradient-based optimization to wind farm layout optimization. To improve model/algorithm compatibility, we adjusted wake and wind farm models, adding more realistic curvature and smoothness to enable optimization algorithms to travel through areas in the design space where they had previously gotten stuck. We reduced the number of function calls required for gradient-based wind farm layout optimization by over three orders of magnitude for large farms by using algorithmic differentiation to compute derivatives. We reduced the multi-modality of the wind farm layout design space using wake expansion continuation (WEC). We developed WEC to work with existing optimization algorithms, enabling them to get out of local optima while remaining fully gradient-based. Across four case studies, WEC found results with lower wake loss, on average, than the other methods we tested. To resolve concerns about optimization algorithms exploiting model inaccuracies, we compared the initial and optimized layouts to large-eddy simulation (LES) results. The simple models predicted an AEP improvement of 7.7% for a low-TI case, and LES predicted 9.3%. For a high-TI case, the simple models predicted a 10.0% improvement in AEP and LES predicted 10.7%. To resolve uncertainty regarding relative solution quality for gradient-based and gradient-free methods, we collaborated with seven organizations to compare eight optimization methods. Each method was managed by researchers experienced with them. All methods found solutions of similar quality, with optimized wake loss between 15.48 % and 15.70 %. WEC with SNOPT was the only purely gradient-based method included and found the third-to-best solution.