update readme

README.md

@@ -1,15 +1,17 @@
 # A Convexification-based Outer-Approximation Method for Convex and Nonconvex MINLP
 
-This repository contains the benchmark results of the paper **"A Convexification-based Outer-Approximation Method for Convex and Nonconvex MINLP"** by Z. Peng, K. Cao, K.C. Furman, C. Li, I.E. Grossmann, and D.E. Bernal. You can view the detailed results on the [web page](https://github.com/SECQUOIA/Convexification-based-OA-Benchmark/).
+This repository contains the benchmark results of the paper **"A Convexification-based Outer-Approximation Method for Convex and Nonconvex MINLP"** by Z. Peng, K. Cao, K.C. Furman, C. Li, I.E. Grossmann, and D.E. Bernal. You can view the detailed results on the [web page](https://secquoia.github.io/Convexification-based-OA-Benchmark/).
 
-The implementation is based on MindtPy, the Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo. For more information about MindtPy, please refer to the [Pyomo MindtPy documentation](https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html).
+The implementation of the proposed methods is based on **MindtPy**, the Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo. For more information about MindtPy, please refer to the [Pyomo MindtPy documentation](https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html).
 
 The benchmark results for each instance are stored as trace files. For more information about trace files, please refer to the [GAMS trace file documentation](https://www.gams.com/latest/docs/UG_SolverUsage.html#UG_SolverUsage_TraceFile).
 
 The software [Paver 2](https://github.com/coin-or/Paver) is used to process the trace files and analyze the performance of the proposed methods in this work.
 
 ## MINLP Instances
 
+All the MINLP instances benchmarked here are from [MINLPLib](http://minlplib.org), including
+
 1. 435 [convex MINLP instances](https://github.com/SECQUOIA/Convexification-based-OA-Benchmark/minlp_instances/convex_instances.txt)
 2. 182 [nonconvex MINLP instances](https://github.com/SECQUOIA/Convexification-based-OA-Benchmark/minlp_instances/nonconvex_instances.txt)
 

index.md

@@ -7,12 +7,24 @@ title: Benchmark Results
 
 The PAVER reports used in the paper: **"A Convexification-based Outer-Approximation Method for Convex and Nonconvex MINLP"** by Z. Peng, K. Cao, K.C. Furman, C. Li, I.E. Grossmann, D.E. Bernal are available here.
 
+## Implementation
+The implementation of the Outer-Approximation method is based on [MindtPy](https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html), the Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo. Furthermore, we integrate two implementations of the bound tightening and the convexification techniques for MINLP problems in MindtPy. 
+1. The first implementation is based on a special version of [BARON](https://www.minlp.com/baron-solver) 19.4.4, the state-of-the-art commercial MINLP solver.
+2. The second implementation uses [Coramin](https://github.com/Coramin/Coramin) and the FBBT (C++) code in [Pyomo](https://github.com/Pyomo/pyomo), both of which are open-source and offer more flexibility.
+
+## MINLP Instances
+
+All the MINLP instances benchmarked here are from [MINLPLib](http://minlplib.org), including
+
+1. 435 [convex MINLP instances](https://github.com/SECQUOIA/Convexification-based-OA-Benchmark/minlp_instances/convex_instances.txt)
+2. 182 [nonconvex MINLP instances](https://github.com/SECQUOIA/Convexification-based-OA-Benchmark/minlp_instances/nonconvex_instances.txt)
+
 ## Paver Reports
 
-1. [C-OA for convex instances](https://zedongpeng.github.io/Convexification-based-OA-Benchmark/paver_results/convex/multitree)
+1. [C-OA for convex instances](https://SECQUOIA.github.io/Convexification-based-OA-Benchmark/paver_results/convex/multitree)
 
-2. [C-LP/NLP for convex instances](https://zedongpeng.github.io/Convexification-based-OA-Benchmark/paver_results/convex/singletree)
+2. [C-LP/NLP for convex instances](https://SECQUOIA.github.io/Convexification-based-OA-Benchmark/paver_results/convex/singletree)
 
-3. [C-GOA for nonconvex instances](https://zedongpeng.github.io/Convexification-based-OA-Benchmark/paver_results/nonconvex/multitree)
+3. [C-GOA for nonconvex instances](https://SECQUOIA.github.io/Convexification-based-OA-Benchmark/paver_results/nonconvex/multitree)
 
-4. [C-GLP/NLP for nonconvex instances](https://zedongpeng.github.io/Convexification-based-OA-Benchmark/paver_results/nonconvex/singletree)
+4. [C-GLP/NLP for nonconvex instances](https://SECQUOIA.github.io/Convexification-based-OA-Benchmark/paver_results/nonconvex/singletree)