Convex Optimization Notes

1. Duality

1.1 Lagrangian and Dual Function

• Lagrangian for a primal problem:

  $$L(x, \lambda, \nu) = f_0(x) + \sum_{i=1}^m \lambda_i f_i(x) + \sum_{i=1}^p \nu_i h_i(x) $$

• Dual function:

  $$g(\lambda, \nu) = \inf_{x \in D} L(x, \lambda, \nu) $$

  • Always concave (regardless of convexity of primal).
  • Lower bound property: If $\lambda \geq 0$, then $g(\lambda, \nu) \leq p^*$.

1.2 Dual Problem

• Dual problem:

  $$\begin{aligned} &\text{maximize } g(\lambda, \nu) \\ &\text{subject to } \lambda \geq 0 \end{aligned} $$

  • Always a concave problem (even if primal is non-convex).
  • Optimal dual value: $d^*$.

1.3 Weak and Strong Duality

• Weak duality: $d^* \leq p^*$ (always holds).
• Strong duality: $d^* = p^*$ (holds under Slater’s condition for convex problems).

1.4 KKT Conditions

For optimal $(x^*, \lambda^*, \nu^*)$ under strong duality:

1. Primal feasibility: $f_i(x^*) \leq 0, \quad h_i(x^*) = 0$.
2. Dual feasibility: $\lambda_i^* \geq 0$.
3. Complementary slackness: $\lambda_i^* f_i(x^*) = 0$.
4. Stationarity: $\nabla_x L(x^*, \lambda^*, \nu^*) = 0$.

• For convex problems, KKT is necessary and sufficient for optimality.

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2. Applications in Machine Learning

2.1 Logistic Regression

• Convex optimization formulation:

  $$\min_w \left[ -\sum_{i=1}^n y_i w^T x_i + \sum_{i=1}^n \log(1 + e^{w^T x_i}) \right] $$

  • Unconstrained convex problem.

2.2 Support Vector Machines (SVM)

• Primal QP:

  $$\begin{aligned} &\min_{w,w_0} \frac{1}{2}\|w\|^2 \\ &\text{s.t. } y_i(w^T x_i + w_0) \geq 1, \quad i=1,\ldots,n \end{aligned} $$

• Dual problem:

  $$\begin{aligned} &\max_{\lambda} \sum_{i=1}^n \lambda_i - \frac{1}{2} \sum_{i=1}^n \sum_{j=1}^n \lambda_i \lambda_j y_i y_j x_i^T x_j \\ &\text{s.t. } \sum_{i=1}^n \lambda_i y_i = 0, \quad \lambda_i \geq 0 \end{aligned} $$

  • Support vectors: points with $\lambda_i > 0$.
  • Kernel trick: Replace $x_i^T x_j$ with $K(x_i, x_j)$.

2.3 Non-Separable Case (Soft Margin)

• Introduce slack variables $\xi_i$:

  $$\begin{aligned} &\min_{w,w_0,\xi} \frac{1}{2}\|w\|^2 + C \sum_{i=1}^n \xi_i \\ &\text{s.t. } y_i(w^T x_i + w_0) \geq 1 - \xi_i, \quad \xi_i \geq 0 \end{aligned} $$

• Dual constraints become: $0 \leq \lambda_i \leq C$.

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3. Neural Network Training & Optimization

3.1 Backpropagation

• Chain rule applied layer-by-layer.
• Local gradients for operations:
  • Add gate: gradient distributor.
  • Multiply gate: gradient swapper.
  • Max gate: gradient router.
  • Copy gate: gradient adder.

3.2 Stochastic Gradient Descent (SGD)

• Update rule:

  $$w_{k+1} = w_k - \eta_k \nabla f_{i(k)}(w_k) $$

• Mini-batch SGD:

  $$w_{k+1} = w_k - \eta_k \frac{1}{|B|} \sum_{j \in B} \nabla f_j(w_k) $$

  • Improves parallelism and stability.

3.3 Advanced Optimizers

• Momentum:

  $$m_k = \beta m_{k-1} + \nabla f(w_k), \quad w_{k+1} = w_k - \eta m_k $$

• Adam: Combines momentum and adaptive learning rates.

3.4 Activation Functions

• ReLU: Most common, but can cause “dead neurons”.
• Sigmoid/Tanh: Suffer from vanishing gradients.
• Leaky ReLU/Maxout: Alternatives to mitigate issues.

3.5 Initialization

• He initialization (for ReLU): $\sigma^2 = 2 / \text{fan-in}$.
• Xavier initialization (for sigmoid/tanh): $\sigma^2 = 1 / \text{fan-in}$.

3.6 Regularization & Techniques

• Dropout: Randomly deactivate neurons during training.
• Batch Normalization: Normalize layer inputs to speed up training.
• Gradient Clipping: Prevent exploding gradients.

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4. Model Selection

4.1 Cross-Validation

• K-fold CV: Partition data into $K$ subsets, train on $K-1$, validate on 1.
• Choose model with lowest cross-validation error.

4.2 Bayesian Model Selection

• BIC:

  $$J_{\text{BIC}}(m) = \log p(D|\hat{\theta}_m) - \frac{d}{2} \log n $$

• AIC:

  $$J_{\text{AIC}}(m) = \log p(D|\hat{\theta}_m) - d $$

• Occam’s Razor: Prefer simpler models if performance is similar.

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5. Statistical Learning Theory

5.1 Generalization Error

• $\Delta(D, h) = R(h) - \hat{R}(D, h)$.
• Overfitting: Small training error, large generalization error.
• Underfitting: Large training and generalization error.

5.2 PAC Learnability

• A hypothesis class $\mathcal{H}$ is PAC-learnable if:

  $$\mathbb{P}\{R(\hat{h}) < R(h^*) + \epsilon\} \geq 1 - \delta $$

  for sufficiently large $n$.
• Finite $\mathcal{H}$: Always PAC-learnable.
• Infinite $\mathcal{H}$: Use VC dimension to characterize learnability.

5.3 VC Dimension

• Definition: Largest set size that can be shattered by $\mathcal{H}$.
• Examples:
  • Linear classifiers in $\mathbb{R}^d$: $VC = d+1$.
  • Axis-aligned rectangles in $\mathbb{R}^2$: $VC = 4$.

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6. Summary

• Duality provides lower bounds and optimality conditions.
• Convex optimization underpins many ML models (SVM, logistic regression).
• Neural networks rely on backpropagation and advanced optimizers.
• Model selection balances complexity and performance.
• Learning theory formalizes generalization and model capacity.