convex-optimization
Problem-solving strategies for convex optimization in optimization
Profile →npx skills add parcadei/Continuous-Claude-v3 --skill convex-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.
# Convex Optimization ## When to Use Use this skill when working on convex-optimization problems in optimization. ## Decision Tree 1. **Verify Convexity** - Objective function: Hessian positive semidefinite? - Constraint set: intersection of convex sets? - `z3_solve.py prove "hessian_psd"` 2. **Problem Classification** | Type | Solver | |------|--------| | Linear Programming | `scipy.optimize.linprog` | | Quadratic Programming | `scipy.optimize.minimize(method='SLSQP')` | | General Convex | Interior point methods | | Semidefinite | CVXPY with SDP solver | 3. **Standard Form** - minimize f(x) subject to g_i(x) <= 0, h_j(x) = 0 - Convert max to min by negating - Convert >= to <= by negating 4. **KKT Conditions (Necessary & Sufficient)** - Stationarity: grad L = 0 - Primal feasibility: g_i(x) <= 0, h_j(x) = 0 - Dual feasibility: lambda_i >= 0 - Complementary slackness: lambda_i * g_i(x) = 0 - `z3_solve.py prove "kkt_conditions"` 5. **Solve and Verify** - `scipy.optimize.minimize(f, x0, constraints=cons)` - Check constraint satisfaction - Verify solution is global minimum (convex guarantees this) ## Tool Commands ### Scipy_Linprog ```bash uv run python -c "from scipy.optimize import li
- When to Use
- Decision Tree
- Tool Commands
- ScipyLinprog
- ScipyMinimize
- Z3Kkt
- Key Techniques
- Cognitive Tools Reference
uv run python -c "from scipy.optimize import linprog; res = linprog([-1, -2], A_ub=[[1, 1], [2, 1]], b_ub=[4, 5]); print('Optimal:', -res.fun, 'at x=', res.x)"
uv run python -c "from scipy.optimize import minimize; res = minimize(lambda x: (x[0]-1)**2 + (x[1]-2)**2, [0, 0]); print('Minimum at', res.x)"
uv run python -m runtime.harness scripts/z3_solve.py prove "kkt_conditions"What does the convex-optimization skill do?
Problem-solving strategies for convex optimization in optimization
How do I install it?
Run `npx skills add parcadei/Continuous-Claude-v3 --skill convex-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.