[ci skip] minor readme

examples/README.md

@@ -7,4 +7,7 @@ A few useful **hints** to explore within Alpine:
 * Choose `presolve_bt` to `false` if you prefer the optimization-based bound tightening (OBBT) presolve to be turned off. 
 * If you prefer to use Alpine for only OBBT presolve, without any paritioning applied to the 
     nonlinear terms, include option `"apply_partitioning"` and set it to `false` while initializing the 
-    Alpine solver. 
+    Alpine solver. 
+
+# Random families of non-convex QCQPs
+Scripts for generating homogeneous families of nonconvex QCQP instances, useful for benchmarking ML-based models for tuning certain algorithmic parameters of global optimization, can be found [here](https://github.com/lanl-ansi/Alpine.jl/tree/master/examples/random_QCQPs). 

examples/random_QCQPs/bilinear/generate_bilinear_instances.py

@@ -4,7 +4,7 @@
 // Reference: "Learning to Accelerate the Global Optimization of Quadratically-Constrained 
 			   Quadratic Programs" by R. Kannan, H. Nagarajan, and D. Deka 
 			   (Link: https://arxiv.org/abs/2301.00306)
-// @author Rohit Kannan <[email protected]>
+// Contributor: @rohitkannan 
 """
 
 import random

examples/random_QCQPs/pooling/generate_pooling_instances.py

@@ -4,7 +4,7 @@
 // Reference: "Learning to Accelerate the Global Optimization of Quadratically-Constrained 
 			   Quadratic Programs" by R. Kannan, H. Nagarajan, and D. Deka 
 			   (Link: https://arxiv.org/abs/2301.00306)
-// @author Rohit Kannan <[email protected]>
+// Contributor: @rohitkannan 
 """
 
 import numpy as np

examples/random_QCQPs/qcqp/generate_qcqp_instances.py

@@ -4,7 +4,7 @@
 // Reference: "Learning to Accelerate the Global Optimization of Quadratically-Constrained 
 			   Quadratic Programs" by R. Kannan, H. Nagarajan, and D. Deka 
 			   (Link: https://arxiv.org/abs/2301.00306)
-// @author Rohit Kannan <[email protected]>
+// Contributor: @rohitkannan 
 """
 
 import random