Docs: adding Introduction to benchmark profiles some more information

tutorials/introduction-to-benchmarkprofiles/index.jmd

@@ -4,11 +4,24 @@ tags: ["benchmarking"]
 author: "Abel Soares Siqueira and Dominique Orban"
 ---
 
-This tutorial is essentially a collection of examples.
+This tutorial demonstrates how to use BenchmarkProfiles.jl to visualize and compare solver performance across multiple test problems.
 
 ## Performance Profile
 
-Performance profiles are straightforward to use. The input is a matrix `T` with entries `T[i,j]` indicating the cost to solve problem `i` using solver `j`. Cost can be, for instance, elapsed time, or number of evaluations. The cost should be positive. If any cost is zero, all measures will be shifted by 1.
+Performance profiles, introduced by Dolan and Moré (2002), provide a graphical way to compare the performance of multiple solvers across a test set. They show the fraction of problems solved by each solver as a function of a performance tolerance.
+
+### Understanding Performance Profiles
+
+The input is a matrix `T` with entries `T[i,j]` indicating the cost to solve problem `i` using solver `j`. Cost can be, for instance, elapsed time, number of iterations, or function evaluations. The cost should be positive. If any cost is zero, all measures will be shifted by 1.
+
+The performance profile plots:
+- **x-axis (τ)**: Performance ratio - how much slower a solver is compared to the best solver for each problem. τ=1 means the solver was fastest, τ=2 means it took twice as long as the fastest solver.
+- **y-axis (ρ(τ))**: Fraction of problems solved within the performance ratio τ. ρ(2) = 0.8 means the solver solved 80% of problems within twice the time of the best solver.
+
+**Key interpretations**:
+- The height at τ=1 (left side) shows the fraction of problems where the solver was fastest
+- The right-side height (as τ→∞) shows the fraction of problems successfully solved (robustness)
+- Higher curves are better - the solver solves more problems with smaller performance ratios
 
 Basic usage:
 
@@ -35,9 +48,19 @@ T[:,2] = 100T[:,3]
 performance_profile(PlotsBackend(), T, ["Solver 1", "Solver 2", "Solver 3"])
 ```
 
+### Customization Options
+
 `Plots` arguments can be passed to `performance_profile()` or used as they normally would be with `Plots`.
 In the example below, we pass `xlabel` to `performance_profile` and set `ylabel` through `ylabel!`.
 
+Common customization options:
+- `lw`: Line width
+- `c` or `color`: Line colors
+- `linestyles`: Line styles (`:solid`, `:dash`, `:dot`, etc.)
+- `xlabel`, `ylabel`: Axis labels
+- `title`: Plot title
+- `legend`: Legend position (e.g., `:bottomright`, `:topleft`)
+
 ```julia
 using Plots
 
@@ -46,3 +69,13 @@ performance_profile(PlotsBackend(), T, ["Solver 1", "Solver 2", "Solver 3"],
       lw=2, c=:black, linestyles=[:solid, :dash, :dot], xlabel="τ")
 ylabel!("ρ(τ)")
 ```
+
+### Additional Parameters
+
+The `performance_profile` function accepts several optional keyword arguments:
+
+- `logscale::Bool=true`: Use logarithmic scale on the x-axis (default: true). Useful for viewing performance across a wide range of ratios.
+- `sampletol::Number=0`: Tolerance for sampling data points. Can reduce plot complexity for large datasets.
+- `title::String=""`: Title for the plot
+
+For more details on performance profiles, see: Dolan, E. D., & Moré, J. J. (2002). Benchmarking optimization software with performance profiles. Mathematical Programming, 91(2), 201-213.