Merge pull request #106 from SciML/Vaibhavdixit02-patch-2

Create gsoc_optimization.md

dev.md

@@ -41,6 +41,7 @@ associated with the project.
 #### Projects Lists
 
 - [General Numerics](/gsoc/gsoc_numerics)
+- [Optimization](/gsoc/gsoc_optimization) 
 - [Numerical Differential Equations and Stochastic Process Simulation](/gsoc/gsoc_diffeq)
 - [Scientific Machine Learning](/gsoc/gsoc_sciml)
 - [Symbolic-Numeric Computing](/gsoc/gsoc_symbolic)

gsoc/gsoc_optimization.md

@@ -0,0 +1,60 @@
+@def title = "SciML Optimization – Google Summer of Code"
+@def tags = ["home", "sciml", "optimization"]
+
+# SciML Optimization Projects – Google Summer of Code
+
+## Generalized multistart optimization strategies
+
+Multistart refers to a global optimization methodology wherein local solvers are run 
+from multiple points in the parameter space and once they converge to local minimas
+this can then be used to either further refine the solution or a minimum operation of the 
+obtained local minimas for getting the global minima.
+
+There has been some work to implement two approaches already namely, a simple quasimontecarlo 
+initialization and using the Particle Swarm Optimization method for some iterations. There is
+scope for coming up with novel methods here as well thus making this project very suitable for
+research focused candidates.
+
+So a generalized implemetation of these strategies, like TikTak and improving the ones mentioned
+above to be compatible with all solvers supported by Optimization.jl will be impactful. This should 
+be done through the EnsembleProblem interface thus leveraging the already available parallelization 
+infrastructure which is essential in these methods for performance.
+
+**Recommended Skills**: Background knowledge in optimization and parallelization.
+
+**Expected Results**: A suite of multistart methods available trhough the SciML EnsembleProblem interface.
+
+**Mentors**: [Vaibhav Dixit](https://github.com/Vaibhavdixit02)
+
+**Expected Project Size**: 175 hour or 350 hour depending on the chosen subtasks.
+
+**Difficulty**: Easy to Hard depending on the chosen subtasks.
+
+## First class OptimizationState to support machine learning optimization better
+
+A core requirement in a lot of optimization tasks involves being able to observe intermediate state
+of the solvers, especially in ML adjacent problems. Recently an OptimizationState object has been added 
+to Optimization.jl for supporting these usecases. There is a lot of scope for making this more performant
+and featureful as well as extending it to more solver wrappers. This project is quite open eneded and can 
+be designed as per the candidates motivation. Some specific things could be utilizing the derivative oracle
+calls to store their values without recomputing it, allowing visualizations of the training loop through
+predefined callbacks, using the state for non-standard stopping criterias in global optimization etc.
+
+**Recommended Skills**: Background knowledge in optimization and familiarity with machine learning workflows.
+
+**Expected Results**: Improvements to OptimizationState and its interactions with solvers.
+
+**Mentors**: [Vaibhav Dixit](https://github.com/Vaibhavdixit02)
+
+**Expected Project Size**: 175 hour or 350 hour depending on the chosen subtasks.
+
+**Difficulty**: Easy to Medium depending on the chosen subtasks.
+
+## More AD goodies, more backends and sparsity support
+
+## Benchmarking suite, automated timing and code profiling
+
+## Tutorials, more problem classes and workflows
+
+
+