Move Makefile to uv and simplified README

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace

Makefile

@@ -1,36 +1,53 @@
+# This Makefile provides commands for setting up the development environment,
+# running formatting tools, and cleaning the repository.
+
+# Set the default target to 'help' when running make without arguments
 .DEFAULT_GOAL := help
 
-VENV :=.venv
+# Create a Python virtual environment using uv (faster alternative to venv)
+.PHONY: venv
+venv:
+	curl -LsSf https://astral.sh/uv/install.sh | sh;
+	@uv venv  # Create a virtual environment in the current directory
 
+# Mark 'install' as a phony target (not associated with a file)
 .PHONY: install
-install:  ## Install a virtual environment
-	python -m venv ${VENV}
-	${VENV}/bin/pip install --upgrade pip
-	${VENV}/bin/pip install -r requirements.txt
+install: venv ## Install a virtual environment
+	@uv pip install --upgrade pip                       # Ensure pip is up to date
+	@uv pip install --no-cache-dir -r requirements.txt  # Install project dependencies from requirements.txt
 
+# Code Quality
 .PHONY: fmt
 fmt: install ## Run autoformatting and linting
-	${VENV}/bin/pip install pre-commit
-	${VENV}/bin/pre-commit install
-	${VENV}/bin/pre-commit run --all-files
+	@uv pip install pre-commit  # Install pre-commit hooks
+	@uv run pre-commit install  # Set up pre-commit hooks
+	@uv run pre-commit run --all-files  # Run pre-commit hooks on all files
 
+# Book Building
 .PHONY: build
 build: install ## Build the book
-	${VENV}/bin/pip install jupyter-book
-	${VENV}/bin/jupyter-book clean book
-	${VENV}/bin/jupyter-book build book
-	touch book/_build/html/.nojekyll
+	@uv pip install jupyter-book  # Install jupyter-book
+	@uv run jupyter-book clean book  # Clean previous builds
+	@uv run jupyter-book build book  # Build the book
+	touch book/_build/html/.nojekyll  # Add .nojekyll file for GitHub Pages
 
+# Cleanup
 .PHONY: clean
 clean:  ## Clean up caches and build artifacts
-	@git clean -X -d -f
+	@git clean -X -d -f  # Remove files ignored by git
 
+# Help
 .PHONY: help
 help:  ## Display this help screen
-	@echo -e "\033[1mAvailable commands:\033[0m"
-	@grep -E '^[a-z.A-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-18s\033[0m %s\n", $$1, $$2}' | sort
+	@echo -e "\033[1mAvailable commands:\033[0m"  # Print header in bold
+	@grep -E '^[a-z.A-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-18s\033[0m %s\n", $$1, $$2}' | sort  # Extract and format targets with comments
 
+# Jupyter Setup
 .PHONY: jupyter
 jupyter: install ## Start jupyterlab
-	${VENV}/bin/pip install jupyterlab
-	${VENV}/bin/jupyter lab
+	@uv pip install jupyterlab  # Install JupyterLab
+	@uv run jupyter lab  # Start JupyterLab server
+
+.PHONY: test
+test: install ## Run the tests
+	@uv run python -m test

README.md

@@ -1,15 +1,16 @@
-# [CVXPY tutorial](https://cvxgrp.org/cvx_short_course)
+# 📊 [CVXPY tutorial](https://cvxgrp.org/cvx_short_course)
+
+## 📋 Overview
 
 CVXPY is an open source Python-embedded modeling language for convex
-optimization problems. It lets you express your problem in a natural way that
-follows the math, rather than in the restrictive standard form required by
-solvers. This tutorial will cover the basics of convex optimization, and how to
+optimization problems. It lets you express your problem naturally, following
+the math, rather than in the restrictive standard form required by
+solvers. This tutorial will cover the basics of convex optimization and how to
 use CVXPY to specify and solve convex optimization problems, with an emphasis on
 real-world applications. No prior knowledge of convex optimization is assumed.
 
-## Getting started
+## 🚀 Getting started
 
 1. Clone this repository.
-2. Install CVXPY following [these instructions](https://www.cvxpy.org/install/index.html).
-We recommend Python 3.10 and CVXPY 1.3.
-3. Test your CVXPY installation by running ``test.py`` in the repository.
+2. Install CVXPY by running ``make install``
+3. Test your CVXPY installation by running ``make test`` in the repository.

test.py

@@ -2,9 +2,12 @@
 import cvxpy as cp
 import numpy as np
 
+print("Starting optimization problem...")
+
 # Problem data.
 m = 30
 n = 20
+print(f"Problem dimensions: m={m}, n={n}")
 np.random.seed(1)
 A = np.random.randn(m, n)
 b = np.random.randn(m)
@@ -16,9 +19,19 @@
 prob = cp.Problem(objective, constraints)
 
 # The optimal objective value is returned by `prob.solve()`.
+print("Solving the optimization problem...")
 result = prob.solve()
+print(f"Optimal objective value: {result}")
+print(f"Problem status: {prob.status}")
+
 # The optimal value for x is stored in `x.value`.
+print("\nOptimal value for x:")
 print(x.value)
+
 # The optimal Lagrange multiplier for a constraint is stored in
 # `constraint.dual_value`.
-print(constraints[0].dual_value)
+print("\nDual values for constraints:")
+print(f"First constraint (0 <= x): {constraints[0].dual_value}")
+print(f"Second constraint (x <= 1): {constraints[1].dual_value}")
+
+print("\nOptimization completed successfully!")