constrained-optimization
Problem-solving strategies for constrained optimization in optimization
Profile →npx skills add parcadei/Continuous-Claude-v3 --skill constrained-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Constrained Optimization ## When to Use Use this skill when working on constrained-optimization problems in optimization. ## Decision Tree 1. **Constraint Classification** - Equality: h(x) = 0 - Inequality: g(x) <= 0 - Bounds: l <= x <= u 2. **Lagrangian Method (Equality Constraints)** - L(x, lambda) = f(x) + sum lambda_j * h_j(x) - Solve: grad_x L = 0 and h(x) = 0 - `sympy_compute.py solve "grad_L_system"` 3. **KKT Conditions (Inequality Constraints)** - Extend Lagrangian with mu_i for g_i(x) <= 0 - Complementary slackness: mu_i * g_i(x) = 0 - `z3_solve.py prove "kkt_satisfied"` 4. **Penalty and Barrier Methods** - Penalty: add P(x) = rho * sum max(0, g_i(x))^2 - Barrier: add B(x) = -sum log(-g_i(x)) for interior point - Increase penalty/decrease barrier parameter iteratively 5. **SciPy Constrained Optimization** - `scipy.optimize.minimize(f, x0, method='SLSQP', constraints=cons)` - constraints = [{'type': 'eq', 'fun': h}, {'type': 'ineq', 'fun': lambda x: -g(x)}] - bounds = [(l1, u1), (l2, u2), ...] ## Tool Commands ### Scipy_Slsqp ```bash uv run python -c "from scipy.optimize import minimize; cons = dict(type='eq', fun=lambda x: x[0] + x[1] - 1); res = minimize(lambda x: x[0]*
- When to Use
- Decision Tree
- Tool Commands
- ScipySlsqp
- SympyLagrangian
- Z3KktSatisfied
- Key Techniques
- Cognitive Tools Reference
uv run python -m runtime.harness scripts/sympy_compute.py solve "[2*x - lam, 2*y - lam, x + y - 1]" --vars "[x, y, lam]" uv run python -m runtime.harness scripts/z3_solve.py prove "complementary_slackness"
What does the constrained-optimization skill do?
Problem-solving strategies for constrained optimization in optimization
How do I install it?
Run `npx skills add parcadei/Continuous-Claude-v3 --skill constrained-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From parcadei/Continuous-Claude-v3, a repository with 3,879 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.