/**
 * Solve Quadratic Programming Problem (Simplified Implementation)
 *
 * Minimizes: ½xᵀQx + cᵀx
 * Subject to: Ax = b (equality constraints)
 *             x ≥ 0   (non-negativity constraints)
 *
 * Uses a simplified gradient descent with constraint projection.
 * This is a practical implementation suitable for portfolio optimization.
 *
 * @param Q - Quadratic coefficient matrix (n×n, symmetric, positive semi-definite)
 * @param c - Linear coefficient vector (n×1)
 * @param options - Solver options and constraints
 * @returns Optimization result with solution vector and metadata
 *
 * @example
 * ```typescript
 * // Portfolio optimization: min wᵀΣw subject to wᵀ1=1, w≥0
 * const result = solveQuadraticProgram(
 *   covarianceMatrix,  // Q = Σ
 *   [0, 0, 0],        // c = 0 (minimum variance)
 *   {
 *     equalityConstraints: { A: [[1,1,1]], b: [1] },  // wᵀ1 = 1
 *     nonNegative: true,                              // w ≥ 0
 *     maxIterations: 1000,
 *     tolerance: 1e-6
 *   }
 * );
 * ```
 */
import { QuadraticProgramOptions } from '../schemas/QuadraticProgramOptionsSchema';
import { QuadraticProgramResult } from '../schemas/QuadraticProgramResultSchema';
export declare function solveQuadraticProgram(Q: number[][], c: number[], options?: Partial<QuadraticProgramOptions>): QuadraticProgramResult;