diff --git a/README.md b/README.md
index 65c34e3..2b419c7 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@
 * D = (X,y) is a dataset derived from f,
 * x* is the true optimum of f in O (minimum or maximum).
 
-The testing framework feeds your algorithm constraints and data (O,D) and collects its predicted optimum. The algorithm's predicted optimal value can then be compared to the true optimal value f(x*). By comparing the two over multiple randomly generated optimization problems, `doframework` produces a **prediction profile** for your algorithm.
+`doframework` feeds your algorithm constraints and data (O,D) and collects its predicted optimum. The algorithm's predicted optimal value can then be compared to the true optimal value f(x*). By comparing the two over multiple randomly generated optimization problems, `doframework` produces a **prediction profile** for your algorithm.
 
 `doframework` integrates with your algorithm (written in Python).
 
@@ -40,7 +40,7 @@ The testing framework feeds your algorithm constraints and data (O,D) and collec
 
 `doframework` can run either locally or remotely. For optimal performance, run it on a Kubernetes cluster. Cloud configuration is currently available for AWS and IBM Cloud [OpenShift](https://docs.openshift.com/ "RedHat OpenShift Documentation") clusters.
 
-The framework relies on Cloud Object Storage (COS) to interact with simulation products. Configuration is currently available for [AWS](https://aws.amazon.com/s3/ "AWS S3") or [IBM COS](https://www.ibm.com/cloud/object-storage "IBM Cloud Object Storage").
+The framework uses storage (local or S3) to interact with simulation products. Configuration is currently available for [AWS](https://aws.amazon.com/s3/ "AWS S3") or [IBM Cloud Object Storage COS](https://www.ibm.com/cloud/object-storage "IBM Cloud Object Storage").
 
 # Install
 
@@ -52,14 +52,23 @@ $ pip install doframework
 
 # Configs
 
-COS specifications are provided in a `configs.yaml`. 
+Storage specifications are provided in a `configs.yaml`. You'll find examples under `./configs/*`.
 
-The `configs.yaml` includes the list of source and target bucket names (under `s3:buckets`). Credentials are added under designated fields.
-
-Currently, two cloud service providers are available under `s3:cloud_service_provider`: `aws` and `ibm`.
-
-`s3:endpoint_url` is optional for AWS.
+The `configs.yaml` includes the list of source and target bucket names (under `buckets`). If necessary, S3 credentials are added under designated fields.
 
+Here is the format of the `configs.yaml` either for local storage
+```
+local:
+    buckets:
+        inputs: '<inputs-folder>'
+        inputs_dest: '<inputs-dest-folder>'
+        objectives: '<objectives-folder>'
+        objectives_dest: '<objectives-dest-folder>'
+        data: '<data-folder>'
+        data_dest: '<data-dest-folder>'
+        solutions: '<solutions-folder>'
+```
+or S3
 ```
 s3:
     buckets:
@@ -75,19 +84,20 @@ s3:
     endpoint_url: 'https://xxx.xxx.xxx'
     region: 'xx-xxxx'
     cloud_service_provider: 'aws'
-
 ```
-**Bucket names above must be distinct**.
+Currently, two S3 providers are available under `s3:cloud_service_provider`: either `aws` or `ibm`. The `endpoint_url` is _optional_ for AWS.
+
+**Bucket / folder names must be distinct**.
 
 # Inputs
 
 `input.json` files provide the necessary metadata for the random genration of optimization problems.
 
-`doframework` will run end to end, once `input.json` files are uploaded to `<inputs_bucket>`. 
+`doframework` will run end to end, once `input.json` files are uploaded to `<inputs-bucket>` / `<inputs-folder>`. 
 
-The jupyter notebook `./notebooks/inputs.ipynb` allows you to automatically generate input files and upload them to `<inputs_bucket>`.
+The jupyter notebook `./notebooks/inputs.ipynb` allows you to automatically generate input files and upload them to `<inputs-bucket>`.
 
-Here is an example of an input file (see input samples `input_basic.json` and `input_all.json` under `./inputs`).
+Here is an example of an input file (see input samples `input_basic.json` under `./inputs`).
 
 
 ```
@@ -102,8 +112,7 @@ Here is an example of an input file (see input samples `input_basic.json` and `i
         },
     },
     "omega" : {
-        "ratio": 0.8,
-        "scale": 0.01
+        "ratio": 0.8
     },
     "data" : {
         "N": 750,
@@ -111,15 +120,14 @@ Here is an example of an input file (see input samples `input_basic.json` and `i
         "policy_num": 2,
         "scale": 0.4
     },
-    "input_file_name": "input.json"
+    "input_file_name": "input_basic.json"
 }
 ```
 
 `f:vertices:num`: number of vertices in the piece-wise linear graph of f.<br>
-`f:vertices:range`: f domain will be inside this box range.<br>
+`f:vertices:range`: f domain will be inside this range.<br>
 `f:values:range`: range of f values.<br>
 `omega:ratio`: vol(O) / vol(dom(f)) >= ratio.<br>
-`omega:scale`: scale of jitter when sampling feasibility regions (as a ratio of domain diameter).<br>
 `data:N`: number of data points to sample.<br>
 `data:noise`: response variable noise.<br>
 `data:policy_num`: number of centers in Gaussian mix distribution of data.<br>
@@ -129,11 +137,11 @@ It's a good idea to start experimenting on low-dimensional problems.
 
 # User App Integration
 
-Your algorithm will be integrated together with `doframework` once it is decorated with `doframework.resolve`. 
+Your algorithm will be integrated into `doframework` once it is decorated with `doframework.resolve`. 
 
-A `doframework` experiment runs with `doframework.run()`. The `run()` utility accepts the decorated model and a path to the `configs.yaml`.
+A `doframework` experiment runs with `doframework.run()`. The `run()` utility accepts the decorated model and an absolute path to the `configs.yaml`.
 
-Here is an example user application `module.py`.
+Here is an example a user application `module.py`.
 
 ```
 import doframework as dof
@@ -148,10 +156,13 @@ if __name__ == '__main__':
     dof.run(alg, 'configs.yaml', objectives=5, datasets=3, **kwargs)
 ```
 
-The testing framework supports the following inputs to your algorithm: 
+`doframework` provides the following inputs to your algorithm: 
 
 `data`: 2D np.array with features X = data[ : , :-1] and response variable y = data[ : ,-1].<br>
 `constraints`: linear constraints as a 2D numpy array A. A data point x satisfies the constraints when A[ : , :-1]*x + A[ : ,-1] <= 0.<br>
+
+It feeds your algorithm additional inputs in kwargs: 
+
 `lower_bound`: lower bound per feature variable.<br>
 `upper_bound`: upper bound per feature variable.<br>
 `init_value`: optional initial value.<br>
@@ -160,26 +171,28 @@ The `run()` utility accepts the arguments:
 
 `objectives`: number of objective targets to generate per input file.<br>
 `datasets`: number of datasets to generate per objective target.<br>
-`feasibility_regions`: number of feasibility regions to generate per objective and dataset.<br>
 `distribute`: True to run distributively, False to run sequentially.<br>
-`logger`: True to see logs, False otherwise.<br>
+`logger`: True to see `doframework` logs, False otherwise.<br>
 `after_idle_for`: stop running when event stream is idle after this many seconds.<br>
+`alg_num_cpus`: number of CPUs to dedicate to your algorithm on each optimization task.<br>
+`data_num_cpus`: number of CPUs to dedicate to data generation (useful in high dimensions).
+
 
 # Algorithm Prediction Profile
 
-Once you are done running a `doframework` experiment, run the notebook `notebooks/profile.ipynb`. It will fetch the relevant experiment products from the target COS buckets and produce the algorithm's prediction profile and prediction probabilities.
+Once you are done running a `doframework` experiment, run the notebook `notebooks/profile.ipynb`. It will fetch the relevant experiment products from the target buckets and produce the algorithm's prediction profile and prediction probabilities.
 
-`doframework` produces three types of experiment products files:
+`doframework` produces three types of experiment product files:
 
 * `objective.json`: containing information on (f,O,x*) 
 * `data.csv`: containing the dataset the algorithm accepts as input
 * `solution.json`: containing the algorithm's predicted optimum
 
-See sample files under `./outputs`/
+See sample files under `./outputs`.
 
 # Kubernetes Cluster
 
-To run `doframework` on a K8S cluster, make sure you are on the cluster's local `kubectl` context. Log into your cluster, if necessary (applicable to OpenShift, see doc).
+To run `doframework` on a K8S cluster, make sure you are on the cluster's local `kubectl` context. Log into your cluster, if necessary (applicable to OpenShift, see `./doc/openshift.md`).
 
 You can check your local `kubectl` context and change it if necessary with
 ```
@@ -189,7 +202,7 @@ $ kubectl config use-context cluster_name
 >> Switched to context "cluster_name".
 ```
 
-Now `cd` into your project's folder and run the setup bash script `doframework-setup.sh`. The setup script will generate the cluster configuration file `doframework.yaml` in your project's folder. The setup script requires the absolute path to your `configs.yaml`. Otherwise, it assumes a file `configs.yaml` is located under your project's folder. Running the setup script will establish the `ray` cluster. 
+Now `cd` into your project's folder and run the setup bash script `doframework-setup.sh`. The setup script will generate the cluster configuration file `doframework.yaml` in your project's folder. The setup script requires the absolute path to your `configs.yaml`. Running the setup `.sh` script will establish the `ray` cluster. 
 
 ```
 $ cd <user_project_folder>
@@ -220,7 +233,7 @@ $ ray submit doframework.yaml module.py
 
 # Ray Cluster
 
-To observe the `ray` dashboard, connect to `http://localhost:8265` in your browser. See the OpenShift doc for OpenShift-specific instructions.
+To observe the `ray` dashboard, connect to `http://localhost:8265` in your browser. See `./doc/openshift.md` for OpenShift-specific instructions.
 
 Some useful health-check commands: 
 
@@ -276,5 +289,5 @@ $ ray submit doframework.yaml doframework_example.py --configs configs.yaml
 ```
 [NOTE: we are using the path to the `configs.yaml` that was mounted on cluster nodes under `$HOME`.]
 
-Make sure to upload input json files to `<inputs_bucket>` once you run `doframework_example.py`.
+Make sure to upload input json files to `<inputs-bucket>` / `<inputs-folder>` once you run `doframework_example.py`.
 
diff --git a/configs/aws_configs.yaml b/configs/aws_configs.yaml
new file mode 100644
index 0000000..13bd60b
--- /dev/null
+++ b/configs/aws_configs.yaml
@@ -0,0 +1,13 @@
+s3:
+  buckets: 
+    inputs: ''
+    inputs_dest: ''
+    objectives: ''
+    objectives_dest: ''
+    data: ''
+    data_dest: ''
+    solutions: ''
+  aws_secret_access_key: ''
+  aws_access_key_id: ''
+  region: ''
+  cloud_service_provider: 'aws'
\ No newline at end of file
diff --git a/configs/dir_configs.yaml b/configs/dir_configs.yaml
index 028470d..bfa1937 100644
--- a/configs/dir_configs.yaml
+++ b/configs/dir_configs.yaml
@@ -6,5 +6,4 @@ local:
     objectives_dest: ''
     data: ''
     data_dest: ''
-    solutions: ''
-    solutions_dest: ''
\ No newline at end of file
+    solutions: ''
\ No newline at end of file
diff --git a/configs/ibm_configs.yaml b/configs/ibm_configs.yaml
new file mode 100644
index 0000000..f855641
--- /dev/null
+++ b/configs/ibm_configs.yaml
@@ -0,0 +1,14 @@
+s3:
+  buckets: 
+    inputs: ''
+    inputs_dest: ''
+    objectives: ''
+    objectives_dest: ''
+    data: ''
+    data_dest: ''
+    solutions: ''
+  aws_secret_access_key: ''
+  aws_access_key_id: ''
+  endpoint_url: ''
+  region: ''
+  cloud_service_provider: 'ibm'
\ No newline at end of file
diff --git a/docs/ocl_lab.md b/docs/ocl_lab.md
new file mode 100644
index 0000000..c88ad06
--- /dev/null
+++ b/docs/ocl_lab.md
@@ -0,0 +1,60 @@
+<!--
+# Copyright IBM Corporation 2022
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+-->
+
+# AAAI 2023 OCL Lab Instructions
+
+Here are the installation instructions for participants of the OCL Lab.
+
+## `doframework` Installation
+
+We recommend installing `doframework` on a designated Python 3.8.0 environment. `doframework` has many dependancies that may override package versions in your current Python environment. 
+
+For example, if you're using `pyenv` in combination with `virtualenv` as your Python environment manager, you can type the following in your terminal
+```
+$ pyenv virtualenv 3.8.0 dof
+$ pyenv local dof
+```
+[Here](https://realpython.com/intro-to-pyenv/#virtual-environments-and-pyenv "pyenv and virtualenv") is a good source on `pyenv` and `virtualenv` by Logan Jones.
+
+Now that you've set up a dedicated Python environment, simply install
+```
+$ pip install doframework
+```
+Run a simple sanity check with
+```
+$ python
+>>> import doframework
+>>> exit()
+```
+The import command may take a while. Once it's finished (successfully, hopefully) you can exit.
+
+## `doframework` Clonning
+
+We will be running `doframework` Jupyter Notebooks as well as using other `doframework` material. Therefore, we'll clone a local copy of `doframework`. From your terminal, run
+
+```
+$ git clone https://github.com/IBM/doframework.git
+```
+To launch the OCL lab Jupyter Notebooks, we'll need to add `jupyter` to our new Python environment
+```
+$ pip install jupyter
+```
+Note that `jupyter` does not come with `doframework`. We want to keep `doframework` light for cloud distribution. Once we're done installing `jupyter`, let's launch the OCL Lab notebooks
+```
+$ cd doframework/notebooks
+$ jupyter notebook
+```
+Now we can begin ...
\ No newline at end of file
diff --git a/doframework/api.py b/doframework/api.py
index 2bbebc6..515dc6d 100644
--- a/doframework/api.py
+++ b/doframework/api.py
@@ -1,5 +1,3 @@
-import os
-import yaml
 import json
 from io import StringIO
 from collections import namedtuple
diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb
index a3ff4f0..c0c2e90 100644
--- a/notebooks/example.ipynb
+++ b/notebooks/example.ipynb
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d490dbde",
+   "id": "692bb66b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ddb5500",
+   "id": "502cc3d2",
    "metadata": {},
    "source": [
     "# DOFramework Example\n",
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39407f1d",
+   "id": "3c88b444",
    "metadata": {},
    "source": [
     "and evaluate $f$ at the vertices of $\\mbox{dom}(f)$."
@@ -153,7 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "8e923df2",
+   "id": "a3dc16c3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "221e2260",
+   "id": "d8025fab",
    "metadata": {},
    "source": [
     "## -- Constraints\n",
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a72eb22e",
+   "id": "4c1046e6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "eae2f001",
+   "id": "19300e2e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dff9634f",
+   "id": "ad02ae20",
    "metadata": {},
    "source": [
     "We'll sample vertics for $\\Omega$ within $\\mbox{dom}(f)$."
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "d9df74df",
+   "id": "2f7c5253",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,7 +236,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "421bcdd8",
+   "id": "94c8d2d4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "6bd2d762",
+   "id": "f1a8d77d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "3a029407",
+   "id": "6bd798c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d341c7de",
+   "id": "ea770d16",
    "metadata": {},
    "source": [
     "We can use the PWL object $f$ to sample points in its domain."
@@ -285,8 +285,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c185e381",
+   "execution_count": 14,
+   "id": "4134e870",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80c387f3",
+   "id": "e0fc31d7",
    "metadata": {},
    "source": [
     "or evaluate points"
@@ -303,17 +303,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "52e1182c",
+   "execution_count": 15,
+   "id": "e28fee3f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.09292907, -1.02811853,  0.5257558 ])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "f.evaluate(xs)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a632859c",
+   "id": "59f666dc",
    "metadata": {},
    "source": [
     "## -- Ground Truth\n",
@@ -323,8 +334,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "df8ed6a3",
+   "execution_count": 16,
+   "id": "9f36a741",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,8 +344,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "31aa42bb",
+   "execution_count": 17,
+   "id": "9a8ad118",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,8 +354,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "93286523",
+   "execution_count": 18,
+   "id": "67788105",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,8 +364,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "19c295d7",
+   "execution_count": 19,
+   "id": "29b6a2f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,8 +374,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "144d0ed1",
+   "execution_count": 20,
+   "id": "3031a9b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "id": "65b88609",
    "metadata": {},
    "outputs": [],
@@ -395,7 +406,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df3ea6c4",
+   "id": "de864d4b",
    "metadata": {},
    "source": [
     "and how much noise to add to functions values in relative terms (```noise=0.05``` means $5\\%$ of $f$ value range in $\\mbox{dom}(f)$)."
@@ -403,8 +414,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "bb4a1602",
+   "execution_count": 22,
+   "id": "654fbb36",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -413,7 +424,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8194728b",
+   "id": "1623a135",
    "metadata": {},
    "source": [
     "We'll sample some means for the Gaussians in the mix from $\\mbox{dom}(f)$."
@@ -421,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "76c948a9",
    "metadata": {},
    "outputs": [],
@@ -431,8 +442,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "9297899c",
+   "execution_count": 24,
+   "id": "35b3ff11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -441,7 +452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2267b8a8",
+   "id": "1fa3f0eb",
    "metadata": {},
    "source": [
     "and sample some non-spherical covariance matrices."
@@ -449,8 +460,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "d3c4dd39",
+   "execution_count": 25,
+   "id": "0e059207",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +470,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f872ca3",
+   "id": "052c0f61",
    "metadata": {},
    "source": [
     "We'll also sample ```weights``` for the Gaussians in the mix."
@@ -467,8 +478,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "4ed58cdc",
+   "execution_count": 26,
+   "id": "c6ef03f3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,7 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82c845dc",
+   "id": "6eb64832",
    "metadata": {},
    "source": [
     "We'll decide on the number of data points $N$ to sample."
@@ -485,8 +496,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "5bff16cf",
+   "execution_count": 27,
+   "id": "6c384255",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12847e06",
+   "id": "7ff15760",
    "metadata": {},
    "source": [
     "and finally get some samples."
@@ -503,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "id": "215be556",
    "metadata": {},
    "outputs": [
@@ -511,7 +522,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-12-11 15:18:34,137\tINFO worker.py:1519 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n"
+      "2022-12-12 11:10:27,508\tINFO worker.py:1519 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n"
      ]
     }
    ],
@@ -521,7 +532,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28840f47",
+   "id": "f263a2d0",
    "metadata": {},
    "source": [
     "We'll make sure all data points are indeed in $\\mbox{dom}(f)$."
@@ -529,8 +540,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "d4ab9d94",
+   "execution_count": 29,
+   "id": "3d3e72f8",
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +550,7 @@
        "True"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -570,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "id": "36bc427e",
    "metadata": {},
    "outputs": [],
@@ -596,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "dd48e7b5",
    "metadata": {},
    "outputs": [],
@@ -618,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "37f214cc",
    "metadata": {},
    "outputs": [],
@@ -628,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "e513a87a",
    "metadata": {},
    "outputs": [],
@@ -669,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "id": "fa7542b3",
    "metadata": {},
    "outputs": [],
@@ -681,18 +692,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "4d605964",
+   "execution_count": 35,
+   "id": "b380c921",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "True optimum: [ 0.62165481 -0.73054224 -0.92673598 -0.39387107]\n",
-      "Predicted optimum: [ 0.6216548  -0.73054224 -0.926736   -0.39387107]\n",
-      "True optimal values: -1.429494476393192\n",
-      "Predicted optimal value: -1.4525388210010832\n"
+      "True optimum: [ 0.04689817 -0.15037332 -0.85110166 -0.718292  ]\n",
+      "Predicted optimum: [ 0.04689817 -0.15037332 -0.85110164 -0.718292  ]\n",
+      "True optimal values: -1.6728688087017412\n",
+      "Predicted optimal value: -1.6223043561609414\n"
      ]
     }
    ],
@@ -718,8 +729,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "506bf769",
+   "execution_count": 36,
+   "id": "597675b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -728,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "id": "7d46d5c0",
    "metadata": {},
    "outputs": [],
@@ -757,7 +768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9952dfd",
+   "id": "9354a90f",
    "metadata": {},
    "source": [
     "To integrate our simple algorithm into ```doframework```, we need to **resolve** it."
@@ -765,8 +776,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "5af09629",
+   "execution_count": 38,
+   "id": "08f68293",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -777,7 +788,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f636898",
+   "id": "3818b15b",
    "metadata": {},
    "source": [
     "## -- Configs\n",
@@ -811,8 +822,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "69b5c623",
+   "execution_count": 39,
+   "id": "b49bf547",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,7 +836,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a5ab5ea",
+   "id": "2393e682",
    "metadata": {},
    "source": [
     "## -- Inputs\n",
@@ -870,7 +881,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aabc20e9",
+   "id": "234d048a",
    "metadata": {},
    "source": [
     "## -- Run\n",
@@ -881,7 +892,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3e353b5c",
+   "id": "f74f829e",
    "metadata": {},
    "outputs": [],
    "source": [